We begin by considering a setting with a single query resource, as in most prior work. We first evaluate the proposed method on the xView dataset with varying search budget  $\mathcal{C} \in \{25, 50, 75\}$  and the number of equal sized grid cells  $N = 49$ . We train the policy with small car as target and evaluate the performance of the policy with the following target classes: Small Car (SC), Helicopter, Sail Boat (SB), Construction Cite (CC), Building, and Helipad. As the dataset contains variable size images, we take random crops of  $3500 \times 3500$  for  $N = 49$ , ensuring equal grid cell sizes. We present the results with different values of  $N$  in the Supplement. The results with  $N = 49$  are presented in Table 1. We observe significant improvements in performance of the proposed PSVAS approach compared to all baselines in each different target setting, ranging from 3 to  $25\%$  improvement relative to the most competitive E2EVAS method. The significance of utilizing supervised information of true labels  $y$ , which are observed after each query at inference time, is supported by the obtained results. This highlights the effectiveness of the PSVAS framework, which enables us to update task-specific prediction module  $f$  by leveraging such crucial information in an efficient manner. The experimental outcomes also indicate two consistent trends. In each target setting, the overall search performance improves as  $\mathcal{C}$  increases, and the relative advantage of MPS-VAS over PSVAS increases, as it is better able to exploit the observed outcomes and in turn improve the policy further for the larger search budget  $\mathcal{C}$ . We also observe that the extent of improvement in performance is greater when there is a greater difference between the target class used in training and the one used during inference. For example, when the target class is a sail boat, the improvement in performance of MPS-VAS in comparison to PSVAS ranges between  $15\%$  to  $35\%$ . However, if the target class is a small car, the improvement in performance of MPS-VAS is only between  $1\%$  to  $2\%$ . Considering all different target settings, performance improvement of MPS-VAS in comparison to PSVAS, ranges from  $1\%$  to  $35\%$ . The results demonstrate the efficacy of the MPS-VAS framework in learning a search policy that enables adaptive search. Figure 3 demonstrates the exploration strategies of different policies that are trained on small car as the target class and test on sail boat as the target. The figure showcases the different exploration behaviors exhibited by each policy in response to the target class, highlighting the impact of the proposed adaptive search framework on the resulting exploration strategies. In Table 2, we present similar results on the DOTA dataset with  $N$  as 64. Here, we train the policy with Large Vehicle as target and evaluate the policy with the following target classes: Ship, Large Vehicle (LV), Harbor, Helicopter, Plane, and Roundabout (RB). We observe that MPS-VAS approach significantly outperforms all other baseline methods across different target scenarios. This shows the importance of MPS-VAS framework for deploying visual active search when search tasks differ significantly from those that are used for training.