Title: Reinforced Active Learning for Large-Scale Virtual Screening with Learnable Policy Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: Virtual Screening

Active Learning

Molecular Activity

Drug Discovery
Abstract: Virtual Screening (VS) is vital for drug discovery but struggles with low hit rates and high computational costs. While Active Learning (AL) has shown promise in improving the efficiency of VS, traditional methods rely on inflexible and handcrafted heuristics, limiting adaptability in complex chemical spaces, particularly in balancing molecular diversity and selection accuracy. 
To overcome these challenges, we propose GLARE, a reinforced active learning framework that reformulates VS as a Markov Decision Process (MDP). Using Group Relative Policy Optimization (GRPO), GLARE dynamically balances chemical diversity, biological relevance, and computational constraints, eliminating the need for inflexible heuristics.
Experiments show GLARE outperforms state-of-the-art AL methods, with a 64.8% average improvement in Enrichment Factors (EF). Additionally, GLARE enhances the performance of VS foundation models like DrugCLIP, achieving up to an 8-fold improvement in  EF$_{0.5\\%}$ with as few as 15 active molecules. These results highlight the transformative potential of GLARE for adaptive and efficient drug discovery.

Reinforced Active Learning for Large-Scale Virtual Screening with Learnable Policy Model

Abstract

Virtual Screening (VS) is vital for drug discovery but struggles with low hit rates and high computational costs. While Active Learning (AL) has shown promise in improving the efficiency of VS, traditional methods rely on inflexible and handcrafted heuristics, limiting adaptability in complex chemical spaces, particularly in balancing molecular diversity and selection accuracy. To overcome these challenges, we propose GLARE 1 , a reinforced active learning framework that reformulates VS as a Markov Decision Process (MDP). Using Group Relative Policy Optimization (GRPO), GLARE dynamically balances chemical diversity, biological relevance, and computational constraints, eliminating the need for inflexible heuristics. Experiments show GLARE outperforms state-of-the-art AL methods, with a 64.8% average improvement in Enrichment Factors (EF). Additionally, GLARE enhances the performance of VS foundation models like DrugCLIP, achieving up to an 8-fold improvement in EF 0 . 5% with as few as 15 active molecules. These results highlight the transformative potential of GLARE for adaptive and efficient drug discovery.

1 Introduction

Virtual Screening (VS) plays a pivotal role in drug design, facilitating the identification of active molecules that interact with biological targets from vast chemical libraries [30]. By predicting the likelihood of activity for each molecule, VS reduces the need for exhaustive experimental testing [43, 62, 40]. However, despite its importance, VS faces significant challenges, including low hit rates (the proportion of active molecules identified) [46] and the computational cost of screening ultra-large libraries ( 10 6 -10 8 compounds) [36, 13]. These limitations have driven the search for more efficient and accurate methodologies to improve the scalability and performance of VS [60].

Active Learning (AL) has shown promise in virtual screening by iteratively refining models through the selective acquisition of informative molecular samples [14, 26]. Popular strategies often involve uncertainty-based, diversity-based, and hybrid approaches [48, 64, 7]. Diversity-based approaches ensure broad exploration by selecting diverse samples, while uncertainty-based methods focus on refining predictions by prioritizing uncertain molecules [5, 55]. However, both of them rely on hand-crafted selection strategies. They are often heavily dependent on the diversity and size of the initial molecular pool, thus lacking the flexibility to dynamically balance exploration and exploitation when new data are acquired [5].

Recent advancements in AL have sought to improve efficiency by introducing frameworks that dynamically combine or select strategies, reducing the need for manual intervention [35, 19]. For instance, SelectAL [15] adjusts AL strategies based on specific tasks and computational budgets, while AutoAL [57] employs a differentiable framework to identify optimal strategies. Although

these approaches mark progress, they remain focused on optimizing the selection of strategies from a predefined set (i.e., "learning to select rules"). This approach does not enable models to directly learn how to identify the most promising candidate molecules (i.e., "learning to select molecules"). As a result, these methods are not well-suited for drug discovery, where the vast and complex chemical space requires highly adaptive and intelligent exploration to effectively balance structural diversity, biological relevance, and computational efficiency [10].

In this work, we introduce GLARE , a G RPO-based L earning framework for A ctive RE inforced screening, designed to overcome the limitations of traditional active learning methods and enhance large-scale virtual screening. GLARE reformulates the virtual screening process as a Markov Decision Process (MDP), enabling Reinforcement Learning (RL) to dynamically optimize molecular selection strategies. By leveraging Group Relative Policy Optimization (GRPO) [49], GLARE eliminates the reliance on manually-designed heuristics, learning to adaptively screen large-scale chemical spaces. By balancing molecular diversity, biological relevance, and computational efficiency, GLARE provides a robust and scalable solution for AL in drug discovery.

Through extensive experiments, we demonstrate that GLARE significantly outperforms state-of-theart AL methods, achieving an average 64.8% improvement in Enrichment Factors (EF). Furthermore, it improves the performance of virtual screening foundation models like DrugCLIP, achieving up to an 8-fold increase in EF 0 . 5% with as few as 15 active molecules given. These results highlight the transformative potential of GLARE in enabling adaptive, efficient, and intelligent molecular selection for drug discovery.

In summary, our contributions are threefold:

We reformulate virtual screening as a Markov Decision Process (MDP), enabling reinforcement learning to dynamically optimize molecular selection.

By utilizing a learnable policy model trained with Group Relative Policy Optimization (GRPO), GLARE eliminates the need for manually-designed heuristics and learns to adaptively screen the complex chemical spaces.

Extensive experiments demonstrate that GLARE significantly outperforms state-of-the-art active learning methods, enhancing virtual screening efficiency and scalability, while also substantially improving the performance of virtual screening foundation models.

2 Related Works

2.1 Virtual Screening Methods

Virtual screening methods are broadly categorized into docking-based and deep learning-based approaches [12]. Docking-based methods [16, 56] use predefined force fields to estimate binding scores, predicting the binding energy, optimal orientation, and conformation of a small molecule within a protein binding site [23]. However, their reliance on rigid scoring functions and static assumptions limits their accuracy and scalability in complex chemical spaces [21].

Deep learning-based virtual screening approaches typically focus on predicting drug-target interactions and binding affinities [34, 61, 32, 28, 63, 39, 29]. These methods represent drugs and target proteins using various encoding techniques, such as molecular fingerprints, SMILES strings, graphbased representations [38, 37] for drugs, and sequence-based or structural embeddings for proteins. These methods leverage large experimental datasets and known interaction information to predict new potential interactions [20, 9, 50], offering a more comprehensive perspective for drug discovery.

However, as the size of chemical and biological data grows, the computational cost and inefficiency of exhaustively labeling data become significant challenges [13, 46]. This highlights the need for active learning to prioritize data points that are most informative for model improvement [42, 41, 14], enabling more effective exploration of the chemical space.

2.2 Active Learning

Active Learning (AL) has been widely applied in drug discovery and development to iteratively guide the selection of unlabeled data for annotation, maximizing model performance with minimal data [14, 26, 5, 55, 8]. Common strategies include uncertainty-based sampling, diversity-based

sampling, and hybrid approaches [48, 64, 7]. For example, Graff et al. [14] explored pool-based AL using uncertainty-based acquisition functions to accelerate virtual screening. Li and Rangarajan [26] proposed diversity-maximizing strategies for graph neural networks, improving chemical space exploration. Van Tilborg and Grisoni [55] systematically evaluated multiple AL strategies under low-data regimes by combining acquisition functions like uncertainty and diversity measures.

Despite these advancements, most AL methods rely on manually designed heuristics or static strategies, limiting their adaptability to dynamic and complex chemical spaces [10]. Recent general AL frameworks such as SelectAL [15] and AutoAL [57] have introduced task-specific optimizations. SelectAL adjusts AL strategies based on computational budgets, while AutoAL employs a differentiable framework to identify optimal strategies.

Other works have attempted to learn acquisition functions using reinforcement learning approaches, primarily based on Q-learning [11, 6]. While these methods have shown promise in domains like semantic and image segmentation, they face challenges in drug discovery, including sparse rewards and the high dimensionality of molecular representations. As a result, they still fall short in navigating vast chemical spaces efficiently under low-data conditions.

In contrast, our work leverages Group Relative Policy Optimization (GRPO) [49], which provides more stable and efficient policy updates compared to Q-learning. GRPO enables dynamic optimization of the acquisition policy, allowing better exploration and exploitation in complex and evolving chemical spaces. By directly addressing the limitations of existing methods, our approach significantly enhances hit discovery efficiency and scalability, particularly in low-data drug discovery scenarios.

3 Methodology

3.1 Problem Definition

Virtual screening aims to identify active molecules that interact with a biological target from a vast molecule library M . The library M contains a large number of unlabeled molecules, with only a small fraction being active. Each molecule m i ∈ M can be labeled as active ( y i = 1 ) or inactive ( y i = 0 ) after annotation. The challenge lies in maximizing the identification of active molecules while minimizing annotation costs.

To address this, active learning is employed. The process iteratively proceeds as follows:

Train : Train the surrogate model ϕ θ using a small labeled dataset D .

Query : Apply a selection strategy ς to choose molecules m select from the unlabeled library M .

Annotate : Label the selected molecules m select using the oracle Ω , yielding m label .

Expand : Add m label to the labeled dataset D .

The iteration continues until the annotation budget is exhausted, with the final labeled dataset D expected to contain a substantial number of active molecules.

Traditional active learning methods rely on predefined selection strategies ς , often referred to as acquisition functions (e.g., greedy algorithms or mutual information maximization [18]). These fixed strategies unable to adapt to the characteristics of different datasets, which limits their generalizability and performance across diverse tasks. In this case, the selection strategy can be expressed as:

where ς ( m,ϕ θ ) scores molecules based on a predefined, hand-crafted acquisition function and ϕ θ is the surrogate model trained on the labeled dataset D .

To address these limitations, we directly replace the predefined selection strategy ς with a learnable policy network π θ , i.e., ς = π θ , which is capable of dynamically adapting to different datasets through training. The selection process in our method is expressed as:

where π θ ( m | D ) represents a probability distribution over the unlabeled molecules M conditioned on the labeled dataset D . Unlike fixed strategies, the policy network π θ is continuously updated during the training step in active learning, adapting as π θ D - → π θ ′ . This adaptability allows the selection strategy to dynamically adjust to different datasets and tasks.

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3.2 Overview of GLARE Framework

Figure 1 presents the framework of the proposed GLARE. Similar to traditional active learning methods, it also includes four steps: Train, Query, Annotate, and Expand. However, in the query stage, GLARE introduces a learnable policy model π θ to adaptively select molecules for annotation after formulating a Markov Decision Process (MDP), addressing the diverse and complex nature of virtual screening tasks. The policy model π θ consists of a Molecular Encoder, which extracts key features from the molecule library M , and an Action Scoring Layer, which selects molecules for annotation based on these features. To optimize the policy model and enhance overall performance, GLARE employs Group Relative Policy Optimization (GRPO), balancing structural diversity, biological relevance, and computational efficiency, ensuring adaptive and efficient molecular selection.

3.3 Reinforcement Learning for the Policy Model

The optimization task of the policy model π θ is reformulated as a Markov Decision Process (MDP) within the framework of reinforced active learning-based virtual screening. In this setup, candidate molecules are divided into a series of groups, and during each active learning iteration, the policy model sequentially selects molecules for annotation. The MDP is defined by the following key components:

State: The state s i represents the embedding x i of the molecule m i in the group that currently being considered for annotation.

Action: The action a i = [ p e , p s ] corresponds to the probabilities of "exclude" ( p e ) and "select" ( p s ) for a molecule m i . The policy model π θ determines the action for each molecule based on its state s i , denoted as π θ ( a i | s i ) .

Reward: The reward evaluates the quality of actions, balancing two critical aspects:

Exploitation : Reflects the immediate utility of the selected molecules, specifically whether they are active.

Exploration : Captures the long-term benefit of selecting molecules, providing insights into the broader library, even for inactive molecules.

The reward r i of policy network for a molecule m i is defined as:

where y i represents the label of m i provided by the oracle Ω , and u i is a discount factor that adjusts the exploration contribution which will be discussed in Section 3.4.3.

By framing the optimization task in this manner, the algorithms of molecular selection and model training for GLARE are summarized in Algorithm 1 and Algorithm 2, respectively. After optimization in MDP, the policy model π θ learns to balance structural diversity and biological relevance, enabling adaptive and efficient molecular selection.

̸

3.4 Policy Model for Molecular Selection

The policy model π θ is responsible for determining the probabilities of actions based on the molecular features extracted by the encoder, allowing GLARE to adaptively select molecules for annotation. Given the molecular embedding x i as state s i , the policy network predicts the probability of action a i , denoted as π θ ( a i | s i ) . The policy model consists of Molecular Encoder (Section 3.4.1) and Action Scoring Layer (Section 3.4.2), optimized with GRPO strategy (Section 3.4.3).

3.4.1 Molecular Encoder

To enable the policy model π θ to take actions based on molecules, a molecular encoder f enc is utilized to extract features h i from the input molecules. The encoder f enc is a flexible component that can adapt to various molecular representations. In this study, we consider three types of f enc :

Molecular Fingerprint MLP . Each molecule is represented using a 1,024-bit Extended Connectivity Fingerprint (ECFP) with a radius of 2 Å [44]. These fingerprints are processed by a MultiLayer Perceptron (MLP) to extract molecular features h i :

where σ is the activation function, and W m and b m are learnable parameters of the MLP.

Graph Neural Network (GNN) . For molecules represented as graph structures, a Graph Isomorphism Network (GIN) [59] is implemented. The GIN updates the features of each atom x ij in the molecule m i and an add pooling operation aggregates the features into a molecular hidden feature h i :

where W g is a learnable parameter, ε is a fixed scaling factor, N ( j ) denotes the neighbors of atom j and V ( i ) denotes the set of all atoms in molecule m i .

Pretrained GNN . Pretrained molecular representation models, such as GraphMVP [27] and 3DInfomax [51], can also be adopted as molecular encoders. These pretrained models leverage prior knowledge from large molecular datasets to provide robust initializations, thereby improving training efficiency and enhancing the quality of molecular representations.

3.4.2 Action Scoring Layer

Extracted by the encoder, the hidden feature h i is passed through a fully connected layer to compute an action score z i for each possible action (e.g., "exclude" or "select"):

where W a and b a are learnable parameters of the action scoring layer.

The scores z i are then transformed into probabilities using a softmax function to gain action a i :

This transformation ensures that the output is a valid probability distribution, where the sum of probabilities for all possible actions equals to 1. The probability distribution represents the confidence of model in selecting or excluding a specific molecule.

3.4.3 Training Strategy Based on GRPO

The goal of the policy model π θ in active learning is to identify the most informative molecules from a group of candidate molecules, which naturally aligns with the objective of the widely used Group Relative Policy Optimization (GRPO) strategy [49]. Thus, we apply an enhanced GRPO to train the policy model π θ with a carefully designed reward function mentioned in Equation 3 based on the requirements of active learning. We further enhance the reward by incorporating uncertainty, ensuring better exploration during molecular selection.

Objective of GRPO in Reinforced Active Learning During the training phase of active learning, the GRPO strategy is adopted to optimize the policy model π θ , maximizing the following objective over a group of candidate molecules G :

where π ref , π old, and π θ are the policy model in previous active learning iteration, previous training epoch, and current epoch, respectively. clip ( · ) is a clip operation for stabilizing training and ϵ is the clip ratio [47]. The definition of advantage A i and KL divergence D KL [ · ] as follows:

where r is the reward vector of the group of candidate molecules G .

The GRPO objective ensures that the policy model π θ focuses on selecting the most informative molecules while maintaining training stability. Additionally, the KL divergence term prevents overfitting, especially when meeting the limited available training data in active learning due to the annotation budget.

Exploration Modification Although the sampling process of the policy model π θ already incorporates randomness (as discussed in Section 3.4.2), explicitly considering the value of exploration in the reward function further enhances its ability to avoid aimless or uninformative exploration [5]. This is achieved by introducing gradient-based uncertainty to estimate the exploration value and designing a discount factor u i to modify the reward.

The discount factor u i is defined as:

̸

where ν is the normalized 2-norm of the gradient g y i , which is defined as:

As defined in [2], molecules with higher uncertainty yield greater [ p e i -I ( y i = 0)] 2 +[ p s i -I ( y i = 1)] 2 , resulting in greater 2-norm values of the gradient g y i . The detailed derivation is provided in the Appendix A.

By introducing the discount factor u i , the model reduces the negative reward for inactive molecules with high uncertainty. This adjustment ensures such molecules can still contribute valuable information for model training and exploration despite being inactive.

4 Experiments

4.1 Experimental Setup

Datasets . We evaluate GLARE on two widely used virtual screening benchmarks: LIT-PCBA [52] and Enamine [1]. For LIT-PCBA, we conduct experiments on the three subsets (ALDH1, PKM2, and VDR) with most experimentally validated molecules adhering to the protocols in [55]. Each subset is allocated a total annotation budget of 1% (1,000 molecules) of the library (100,000), with 64 molecules annotated per iteration. Enamine is a large-scale screening database with subsets named Enamine50k and EnamineHTS. the total annotation budget of Enamine50k is set as 6% (3,000) of the library (50,240), with 1% (500) per active learning iteration. Due to the large scale of EnamineHTS, the budget is reduced to 0.6% (12,600) and 1.2% (25,200) of the library (2,141,514), with 0.1% (2,100) and 0.2% (4,200) per iteration, noted as EnamineHTS-0.1 and EnamineHTS-0.2, respectively.

Baselines . We evaluate GLARE against two state-of-the-art active learning methods for virtual screening: TcsAL [55], with two model architectures and diverse acquisition functions, and PtAL [5], which leverages pretrained models like MoLFormer [45] and MolCLR [58]. Additional baselines include D-MPNN [17], RF [3], and LightGBM [24], with acquisition functions such as greedy, Mutual Information (MI) [18], Upper Confidence Bound (UCB), uncertainty, similarity, and random.

Metrics . Enrichment Factor (EF) is used to evaluate virtual screening performance, calculated as EF = | D active | | D | / | M active | | M | , where D active and M active represent active molecules in the selection and entire library, respectively. Retrieving Rate (RR n ) measures the ability to retrieve the highly active molecules, defined as RR n = | D topn | n , where | D topn | is the number of top n active molecules selected.

4.2 Evaluation of Active Learning for Virtual Screening

4.2.1 Evaluation on General Benchmarks

We evaluate GLARE on general active learning virtual screening benchmarks using three subsets, ALDH1, PKM2, and VDR from LIT-PCBA, following the protocols in [55]. Table 1 reports the Enrichment Factor (EF) scores at the mid-phase (10th iteration) and final-phase (16th iteration). Results for all iterations are shown in Appendix B.1. GLARE consistently outperforms all baseline methods, achieving significant improvements. Notably, GLARE with a pretrained GNN achieves the highest EF scores of 7.205, 7.768, and 7.992 on ALDH1, PKM2, and VDR, reflecting improvements of 27.0%, 90.9%, and 76.5%, respectively, over the best baseline. Even without a pretrained model, GLARE with MLP and GNN significantly exceeds the best baseline in almost all scenarios, showcasing its strong performance across various molecular encoders. These findings highlight that learning a policy model for adaptive molecular selection strategies, rather than relying on predefined functions, substantially enhances performance.

4.2.2 Evaluation on Large-scale Benchmarks

To further validate the scalability of GLARE, we conducted experiments on larger-scale virtual screening benchmarks. Table 2 presents the RR n scores of various methods at the mid-phase (4th iteration) and final-phase (6th iteration) of active learning. More details are shown in Appendix B.2.

Among all the methods, GLARE with a pretrained GNN achieves the best performance, benefiting from faster initialization, which enables the model to effectively identify active molecules early in the active learning process. On Enamine50k, GLARE with a pretrained GNN achieves RR 500 scores of 0.7765 and 0.8869 at the mid and final phases, representing improvements of 28.3% and 11.9% over the best baseline (PtAL with MoLFormer and UCB). Furthermore, GLARE demonstrates excellent efficiency on the larger EnamineHTS, achieving RR 1 , 000 scores of 0.9181 (EnamineHTS0.1) and 0.9732 (EnamineHTS-0.2), with substantial improvements over the best baseline. Although underperform at the beginning, GLARE with GNN surpasses all non-pretrained methods and PtAL with MolCLR at final-phase, despite MolCLR being a pretrained GNN, which further validate the superiority of our adaptive selection strategy. These experiments on large-scale benchmarks clearly demonstrate the scalability and effectiveness of GLARE for large-scale virtual screening tasks.

4.3 Improvement over Foundation Virtual Screening Methods

To further showcase the practicality of GLARE, we apply it to enhance the performance of virtual screening foundation models with a limited number of active molecules. The experiment is conducted on the entire LIT-PCBA dataset using the pretrained DrugCLIP [12], with a budget of n active molecules, denoted as GLARE( n ).

Table 3 summarizes the results of various virtual screening methods on the LIT-PCBA. Impressively, the screening performance improves by 46.7% on EF 0 . 5% even with the addition of just a single active molecule during active learning. With 15 additional active molecules, GLARE(15) achieves an 8-fold improvement in EF 0 . 5% compared to the baseline model DrugCLIP. These results highlight that our method not only enables efficient virtual screening but also significantly enhances the capability of existing foundational virtual screening models with a minimal budget of active molecules.

4.4 Ablation Studies and Visualization

Ablation studies (Figure 2.a) demonstrate the effectiveness of the GRPO-based reinforced active learning in GLARE. GLARE w/o AL discards the active learning process, while GLARE w/o Policy adopts an active learning process but without reinforcement learning. Both of them perform significantly worse, even with a larger annotation budget. Experiments on batch size and total annotation budget reveal that medium batch sizes (64 and 128) strike the best balance between efficiency and performance, while excessively small or large batch sizes lead to trade-offs between screening time and effectiveness (Figure 2.b and c). More analysis is shown in Appendix B.4.

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The UMAP [31] visualization (Figure 2.d) shows GLARE explores a broader and more diverse chemical space compared to TcsAL, achieving higher hit rates at both the mid-phase (10th iteration) and final-phase (16th iteration) of active learning. This highlights the superior adaptability and effectiveness of GLARE in discovering novel active molecules while maintaining high structural diversity.

5 Discussion

This work presents GLARE, an active learning-based virtual screening framework that uses a GRPObased strategy to efficiently explore chemical space with a limited annotation budget. Experiments show the superior performance of GLARE, significantly improving EF 0 . 5% and hit rates on datasets like LIT-PCBA and ALDH1, while visualizations highlight its ability to identify diverse and novel active molecules. GLARE enhances molecule discovery efficiency and reduces costs, making it a valuable tool for drug discovery. However, GLARE faces challenges when scaling to ultra-large chemical spaces (e.g., 10 10 molecules) due to increased time costs, and its performance tends to decline as the annotation budget increases. Future work could focus on improving scalability and mitigating the diminishing returns associated with larger annotation budgets.

Acknowledgments and Disclosure of Funding

This study has been supported by the National Natural Science Foundation of China [T2394502], the Guangdong S&T Program [2024B1111140001], the National Natural Science Foundation of China [62302537], the China Postdoctoral Science Foundation [2025M771540, GZB20250391], the Guangzhou Basic and Applied Basic Research Foundation [2024A04J4449], and the Lingang Laboratory [LGL-8888].

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A Proof for Uncertainty Estimation Based on Gradient

The policy model can be simply modeled as π θ ( a i | s i ) = W · w ( s i ) + b , then cross entropy loss between a i = [ p e i , p s i ] and y i is:

We define g y i k = ∂ ∂W k l CE ( a i , y i ) as the gradients corresponding to y k :

Replacing y i with ˆ y i = arg max ( a i ) , then the 2-norm of whole gradient g ˆ y i = ∂ ∂W l CE ( a i , ˆ y i ) is:

Evidently, high-uncertainty samples yield greater [ [ p e i -I (ˆ y i = 0)] 2 +[ p s i -I (ˆ y i = 1)] 2 ] since ˆ y i = arg max ( a i ) , i.e., the length of g ˆ y i is long. Therefore, samples with high uncertainty tend to have a great 2-norm of the gradient.

B Additional Results

B.1 Detailed Results of General Benchmarks

Table 4 presents the complete results of GLARE on three LIT-PCBA subsets: ALDH1, PKM2, and VDR, covering a total of 16 iterations, with an annotation budget of 64 allocated for each iteration.

B.2 Detailed Results of Large-scale Benchmarks

Table 5 presents the complete results of GLARE on Enamine50k and EnamineHTS, covering a total of 6 iterations. For Enamine50k, the annotation budget is 1% (500) of the library per active learning iteration. For EnamineHTS, the annotation budget is 0.6% (12,600) and 1.2% (25,200) of the library per active learning iteration, noted as EnamineHTS-0.1 and EnamineHTS-0.2, respectively.

B.3 Detailed Results with Standard Deviations

To further validate the stability and robustness of the proposed method, Table 6 presents the detailed experimental results of GLARE on ALDH1, PKM2, and VDR, including the mean performance metrics and their standard deviations (Mean ± STD). All results were obtained from multiple independent runs to ensure the reliability and statistical significance of the evaluation.

B.4 Supplement of Ablation Studies

To investigate the effectiveness of active learning, we train two variant models, denoted as GLARE w/o AL and GLARE w/o Policy. Considering maintaining the same size of training data, we design GLARE w/o AL with 1000 labeled samples to start and an additional 1000 budget for screening. GLARE w/o Policy adopts an active learning process but without reinforcement learning. As Table 7 shows, although using twice the annotation budget, GLARE w/o AL performs the worst, which can be attributed to the absence of active learning, leading to a substantial waste of annotation resources. In addition, GLARE w/o Policy also underperforms compared to GLARE due to the lack of an adaptive molecular selection strategy. These results demonstrate the advantage of active learning-based virtual screening over conventional one-shot screening.

We investigate the effects of annotation budget per iteration (i.e., batch size) and total budget in active learning, using batch sizes of 32 / 64 / 128 / 256 and budgets of 500 / 1,000 / 2,000. As shown in Table 8, EFs for batch sizes 64 and 128 initially increase but slightly decline as the budget grows. The initial increasement can be attributed to the reason that larger budget facilitates better model training and allocates more resources for screening. But the subsequent slight decline occurs because the easily identifiable active molecules are quickly selected in the earlier iterations, leaving behind more challenging ones. Due to the large batch size of 256, only a few active learning iterations can be operated, resulting in inferior performance compared to others. EF of batch size 32 is the greatest when annotation budget comes to 500, because smaller batch size allows the model to update without delay and avoid wasting the screening budget. However, this comes at the cost of doubled screening time (Figure 2.c), with minimal EF improvement when the annotation budget exceeds 1000.

B.5 Time Consumption

To provide a comprehensive comparison, we evaluated both training and inference runtimes on the ALDH1 dataset, using the same hyperparameters and hardware as TcsAL [55].

Active learning typically involves training on a small number of labeled samples while making predictions on a much larger pool of unlabeled data. As a result, inference time becomes the dominant computational cost, especially in large-scale virtual screening scenarios. As shown in the Table 9, while GLARE requires extra training time due to the computational cost of gradient-based uncertainty in reward calculation, this overhead is confined to the training phase. During inference, reward computation is not required, enabling GLARE to achieve inference speeds comparable to TcsAL.

The characteristic is particularly important for large-scale dataset. To further demonstrate the scalability of our approach, we tested it on the AmpC dataset ( 10 8 compounds) using the same hyperparameters as PtAL[5]. As shown in the Table 10, GLARE achieves inference speeds comparable to the baseline PtAL across both medium (EnamineHTS-0.1, 2 million compounds) and ultra-large (AmpC, 99.5 million compounds) datasets. As dataset size increases, inference time becomes even more significant. Our method maintains inference speed similar to baseline methods while delivering much higher accuracy, making it especially well-suited for ultra-large virtual screening tasks.

B.6 Detailed Results of Training Loss

Regarding training convergence, we ensured sufficient training within each active learning round. Following the TcsAL baseline, we trained each model for 50 epochs per iteration and monitored the training loss. As shown in the Table 11, the loss curves for MLP, GNN, and Pre. GNN all reach convergence within each iteration, confirming that our models are well-trained.

B.7 Comparison for Different Pretrained GNN

To further isolate the effect of the learning strategy, we compared GLARE (with MolCLR) and PtAL (also with MolCLR) under same pretrained GINs(i.e. MolCLR), shown in the Table 12. GLARE consistently outperforms PtAL, showing that the performance gain is primarily due to GLARE's superior learning strategy rather than the encoder alone.

B.8 Different Optimization Strategies and Virtual Screening Methods

We experimented with Direct Policy Optimization (DPO), another effective RL algorithm, for optimizing the policy network. As shown in the Table 13, GRPO consistently outperforms DPO in our setting. We attribute this to the utilization of group-wise advantage estimation in GRPO, which better captures the relative quality of selected molecules within a batch (a property that aligns well with the objectives of virtual screening). This provides a direct motivation for our choice of GRPO.

Recently TTA has been applied in virtual screening, which typically leverages self-supervised auxiliary tasks during inference to dynamically update the model parameters, enabling the learning of feature representations tailored to individual test instances. This approach requires no prior knowledge of the test data distribution and instead adjusts model parameters dynamically on a per-instance basis during inference. Experiments about the comparison with DrugTTA[50] baseline are conducted. The results are summarized is Table 14.

C Additional Details

C.1 Overview of the Benchmarks

LIT-PCBA PCBA includes a total of 15 targets, comprising 7,844 active and 407,381 inactive compounds. Following [55], we selected the three LIT-PCBA datasets with the highest numbers of experimentally validated molecules, which correspond to targets of clinical and therapeutic interest: pyruvate kinase M2 (PKM2, agonism), aldehyde dehydrogenase 1 (ALDH1, inhibition), and vitamin D receptor (VDR, antagonism). For each dataset, 100,000 molecules were randomly sampled while maintaining the ratio of active to inactive compounds, which were used to construct a screening library. For details, refer to Table 15.

Enamine Enamine is a commercially available database for large-scale screening. The Enamine Discovery Diversity Set (Enamine50k) and Enamine HTS collection (EnamineHTS) consist of 50,240 compounds and 2,141,514 molecules, respectively. The Enamine datasets used for these studies were generated from docking the compounds against thymidylate kinase (PDB ID: 4UNN). Both Enamine50k and EnamineHTS are publicly accessible via the MolPAL code repository[14], undergo molecular docking against thymidylate kinase (PDB ID: 4UNN)[33] using AutoDockVina[53].

C.2 Baseline Methods

Baselines We consider two challenging baselines for a thorough evaluation, which contain a range of surrogate models and acquisition functions as traditional active learning methods. Van Tilborg and Grisoni [55] proposes an active learning virtual screening baseline containing two model architectures and some acquisition functions (for convenience, we name it as TcsAL). Cao et al. [5] conducted experiments on large-scale datasets and proposed a large-scale active learning virtual screening baseline incorporating pretrained models (for convenience, we name it as PtAL).

Pretrained Models The pretrained models used in PtAL include MoLFormer [45] and MolCLR [25]. MoLFormer is a transformer-based model that takes tokenized SMILES strings as input and learns molecular representations by capturing intrinsic spatial relationships between atoms. To improve computational efficiency, it employs linear attention and rotary position embedding instead of standard quadratic attention. MoLFormer was pretrained on an ultralarge dataset of 1.1 billion small molecules from ZINC and PubChem using the mask-language-modeling technique. MolCLR is a graph isomorphism network pretrained on 10 million molecules from PubChem [25] using a contrastive learning strategy. Other comparative models include D-MPNN [17], a GNN variant, as well as RF [3] and LightGBM [24], which are decision tree-based ensemble methods.

Acquisition Functions Acquisition functions used for comparison comprise greedy, mutual information (MI) [18], upper confidence bound (UCB), uncertainty, similarity, and random.

To estimate the expected value E of a molecule m i , the mean prediction across all surrogate models in the ensemble is considered when performing traditional active learning-based virtual screening:

Similarly, the prediction uncertainty for a molecule m i is defined as the mean entropy H over the ensemble:

Following these definition, the acquisition functions are defined as follows.

Similarity: samples are selected on the basis of their highest Tanimoto coefficient (computed with ECFPs; with 1,024 bits and a radius of 2) to any previously acquired hit compound.

Greedy: the best predicted samples are selected with

Uncertainty: most uncertain samples are selected with

Mutual Information (MI) [18]: selects samples with low mutual information with

Upper Confidence Bound (UCB): selects samples with the highest upper confidence bound

where V denotes the standard deviation between the models.

C.3 Evaluation Metrics

BEDROC Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC)[54] is designed to assess early recognition performance, giving higher weights to active compounds that are ranked closer to the top. The formal definition is:

The commonly used variant is BEDROC 85 , where the top 2% of ranked candidates contribute to 80% of the BEDROC score.

Enrichment Factor (EF α ) For evaluating early retrieval, we use EF α defined as:

where M α is the true active molecules in top α % , M is the whole library.

C.4 Implementation Details

Hyper-parameters We used the Adam optimizer with a learning rate of 3e-4. The training batch size was set to 64 and the inference batch size was set to 512 for quicker inference. The embedding dim of molecules is 130, and the hidden dim is 1024. The MLP and GIN employed in the molecular encoder have 3 layers. An MLP with 3 layers is also used in the action scoring layer. During the training step of active learning, the number of training epochs is set to 50. The ϵ and β in GRPO are set to 7e -2 and 1e -2 , respectively.

Hardware The experiments were performed on a computational cluster equipped with dual Intel Xeon Gold 6248R CPUs (3.00 GHz, 48 cores) and an NVIDIA RTX 4090 GPU with 24 GB of memory, running Ubuntu 22.04.2 LTS as the operating system.

NeurIPS Paper Checklist

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Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: We have described in Appendix C.4.

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The answer NA means that the paper does not include experiments.

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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: We have provided the broader impacts of our work in the introduction section. Extensive experiments show that GLARE outperforms state-of-the-art active learning methods, boosting virtual screening efficiency, scalability, and foundation model performance.

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Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

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Answer: [NA]

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The answer NA means that the paper does not use existing assets.

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Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: We introduce a reinforced active learning framework GLARE for virtual screening, and we have provided the details of our method in this paper.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GLARE, a novel active learning framework for drug virtual screening. Unlike traditional methods that use hand-crafted selection heuristics, GLARE learns a reinforcement learning-based policy to directly select molecules for labeling. By formulating the task as a Markov Decision Process and optimizing a policy using Group Relative Policy Optimization (GRPO), GLARE dynamically balances exploration (diversity) and exploitation (biological relevance). Extensive experiments show that GLARE significantly outperforms existing active learning methods, achieving up to 64.8% higher enrichment factors and enhancing the performance of foundation models like DrugCLIP by up to 8-fold with minimal labeled data.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n1. The overall framework design is well-aligned with the virtual screening task. Reformulating the process as a Markov Decision Process and learning a molecule selection policy is a reasonable and principled approach.\n2. The proposed method shows consistent improvement over baseline models across several datasets and evaluation metrics. \n3. The paper is clearly written and easy to follow. The figures, including framework diagrams and UMAP visualizations, are clear, professional。\n\n## Weakness\n1. While the approach is effective, the overall idea is relatively straightforward. It applies existing reinforcement learning techniques (specifically GRPO) within an active learning setting. The novelty mainly lies in combining known components. It would be beneficial to evaluate other reinforcement learning algorithms beyond GRPO, or provide stronger justification for its selection.\n2. The policy model only considers molecular features when making selection decisions. In structure-based virtual screening, incorporating protein or pocket-level features might improve performance.\n3. The method appears to require training a separate policy model for each target, which limits scalability in applications involving large sets of proteins or binding pockets. It does not support efficient many-to-many matching.\n4. The baselines in the experiments are somewhat limited. Only TcsAL is used for the LIT-PCBA benchmark, and only PtAL for the Enamine datasets. It would strengthen the empirical evaluation to include a wider range of baseline methods.\n\nQUESTIONS:\nsee weakness\n\nLIMITATIONS:\nn/a", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-23"}

comments: SUMMARY:
This paper presents GLARE, a novel active learning framework for virtual screening. The authors reformulate the selection of molecules as a Markov Decision Process (MDP) and apply Group Relative Policy Optimization (GRPO) to train a learnable policy model for molecule selection. The proposed framework achieves state-of-the-art performance over baselines along with better exploration and exploitation.

STRENGTHS AND WEAKNESSES:
**Stength:**
1. This paper studies an important scientific problem, i.e., virtual screening, with potential real-world impact
2. Reformulating virtual screening as an MDP is innovative
3. The writing is overall clear and easy to follow.


**Weakness:**
1. The RL part relies heavily on Group Relative Policy Optimization, which limits the novelty of the paper
2. The paper does not report walltime or computational cost relative to TcsAL or PtAL. 
3. The choice of pretrained GNN is not clear. If I understand correctly GNN corresponds to GIN, how about the pretrained GNN? And similarly, for table 2 could author add another experiment so that PtAL and GLARE are using same pretrained GNNs for a more fair comparison?

QUESTIONS:
1. Could GLARE be extended to support multi-objective optimization? for example, if users want to identify active molecules with optimized druglikeness, solubility, etc. Could the reward function or policy network be extended accordingly?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes GLARE, a reinforcement learning-based active learning (AL) framework designed for large-scale virtual screening under limited annotation budgets. GLARE reformulates molecular selection as a Markov Decision Process (MDP) and replaces traditional hand-crafted acquisition strategies with a learnable policy network trained via Group Relative Policy Optimization (GRPO). The method dynamically balances structural diversity, biological relevance, and computational cost during molecule selection. The framework is compatible with various molecular encoders, including MLPs, GNNs, and pretrained models. Experiments on the LIT-PCBA and Enamine benchmarks shown substantial improvements in enrichment factor (EF) and retrieving rate (RR) over competitive baselines, particularly in low-data regimes. GLARE also shows strong synergy with foundation models like DrugCLIP, achieving multi-fold gains with minimal additional annotations. Overall, the authors contribute an adaptive approach for efficient hit identification in early-stage drug discovery.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. The use of a learnable policy model optimized through GRPO distinguishes this work from previous solutions that rely on static and handcrafted heuristics. The methodology is solid as the paper provides a thorough breakdown of the components, including the molecular encoder variants, reward function design, and policy optimization.\n2. The experimental section is extensive, with evaluations on both standard (LIT-PCBA) and large-scale (Enamine) benchmarks. The framework performs well even in extremely low-data regimes, and it is also able to improve the enrichment factor of foundation models like DrugCLIP with limited additional labels. These promising results (Section 4, Experiments) show a great potential of GLARE in early-stage drug-discovery practice.\n3. The inclusion of ablation studies, UMAP visualizations, and comparisons across multiple molecular encoders add clarity and depth to the evaluation.\n\nWeakness:\n1. Although the GRPO-based optimization is well-motivated, the computational cost associated with reinforcement learning in high-dimensional chemical spaces is not clearly quantified (Section 5, Discussion), particularly as the size of the screening library increases to 10⁸–10⁹ compounds.\n2. The reward function in equation 3 and equation 10 uses uᵢ, a manually constructed discount factor for exploration. This heuristic rule proves effective on specific datasets, but it may not generalize well to other tasks (e.g., different biological targets or hit rate distributions), as it is fundamentally driven by empirical design rather than data.\n3. The contributions of the pretrained encoder versus the learned policy in GLARE are not clearly distinguished. In Table 1 and Table 2, although the combination of GLARE with pretrained GNN encoders performs best, the experiments do not clarify whether the observed performance gains arise primarily from the pretrained molecular representations or from the optimization of the policy network itself.\n\nQUESTIONS:\n1. Can the authors provide a quantitative analysis of GLARE’s computational cost and scalability?\n2. How sensitive is GLARE’s policy performance to the reward formulation and exploration heuristics?\n\nLIMITATIONS:\nNo\nThe paper presents a well-structured and comprehensive framework, but its discussion of limitations could be made more explicit. The authors briefly mention scalability issues (Section 5, Discussion), but they do not provide any quantitative analysis of the computational cost of policy training and inference, especially in ultra large chemical libraries. Next, the reward function includes heuristic components such as uncertainty-weighted discount factors, yet the potential sensitivity of these heuristics across diverse targets is not explored. On top of that, the work does not consider the biological interpretability and off-target risks of the retrieved compounds, which could limit the application of the framework in drug discovery practical scenarios. At this point, a more transparent discussion of these aspects would strengthen the paper’s reliability and completeness.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper introduces the GLARE framework (a GRPO-based Learning framework for Active REinforced screening) for virtual screening in drug discovery. It casts virtual screening as a Markov decision process and integrates it into a policy-based GRPO reinforcement learning framework. GLARE achieves competitive performance across multiple benchmark datasets.

STRENGTHS AND WEAKNESSES:
Strengths: 
- Models virtual screening as a Markov decision process and leverages GRPO for policy optimization.
- Attains higher enrichment factors and retrieval rates compared to classical active‐learning strategies and state‐of‐the‐art pretrained backbones.

Weaknesses:
- Represents an incremental modification of existing reinforcement learning frameworks.
- Does not report training or inference runtime.

QUESTIONS:
- What is the computational time required to train these models?
- Are four and six iterations sufficient for benchmarking the enrichment factor in Table 2? Can the authors show that training has converged?
- Could the authors elaborate on the specific strategies applied in the experiments summarized in Table 2?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03