## 1 Introduction  
After the identification of a biological target (enzyme, receptor, protein, etc. ), the focus of the early phase of drug discovery rests on the identification of leads or compounds that exhibit pharmacological activity against this specific target [1, 2]. This represents the first step in the lengthy process of drug discovery, which includes the phases of target to hit, hit to lead, lead optimization, clinical trials and market launch, followed by the phase four [3]. The process of discovering new drugs based on the knowledge of their biological target is called drug design, where the final goal is a molecule complementary in shape and charge to its target and with a desired pharmacological profile [4]. Computer-aidedmethods have become increasingly important over the years to the extent that they are indispensable in modern drug development. Computer-aided drug design (CADD) generally follows three main objectives: (1) filtering large compound libraries into smaller groups of prospective compounds, (2) optimization of lead compounds and (3) development of novel compounds [5; 6]. The term \"CADD\" is also used for two general approaches, e.g., structure-based and ligand-based drug design (Figure 1). As the name suggests, the structure-based drug design relies on the knowledge of the target structure, while the ligand-based drug design applies the known active and inactive compounds through similarity search, quantitative structure-activity relation (QSAR) studies or through the application of modern machine-learning techniques that are becoming increasingly important in the field [7; 8; 9]. The use of CADD reduces the costs and labor associated with the drug development, but as can be seen in Figure 1, the success of this process depends on the richness of the initial compound library. Due to the sheer size of the entire chemical space (estimates for the number of synthesizable organic compounds vary from \(10^{30}\) to \(10^{60}\)), it is impossible to search it in its entirety. Consequently, extensive compound libraries representing the appropriate chemical space are critical for the success of (virtual) high-throughput screening (vHTS or HTS) [10]. Figure 1: Computer-aided drug design process chart used to obtain lead compounds (based on [PERSON] et al. [5]). This is a computational methodology used to differentiate between the molecules from a virtual library that bind to a potential target and those that do not, as well as to rank these molecules according to their predicted binding strength. This allows us to enrich the hit rate (the number of binding compounds at a given concentration divided by the number of compounds tested experimentally) over classical screening methods, such as the high-throughput screening (HTS) [11]. The popularity of the method is increasing with the larger availability of structural information, such as 3D protein structures, and with the commercially available libraries. The two factors work together in a tandem to provide a broad platform for vHTS [12]. Compared to the classical HTS, which is still used today as a staple for confirming activity against a target, vHTS offers the ability to pre-process enormous lists of molecules, reducing the number of compounds that need to be purchased, synthesized, and tested [13; 14]. For example, the researchers at Pfizer used a vHTS docking protocol to screen for inhibitors of the enzyme tyrosine phosphatase 1B. The vHTS protocol yielded 365 compounds, of which 127 exhibited effective inhibition. Compared to a classical HTS protocol performed in parallel with the vHTS, the hit count for the 400,000 compounds tested was 81, representing a hit rate of only 0.021%, compared to 34.7% in the vHTS protocol [11]. Moreover, the method yields structurally diverse lead compounds and can be extended to compounds that do not yet physically exist but can be obtained by synthesis or that serve as fragments in the later stages of the development [15; 16; 2; 17].