Second, as decoy size increases, computational speed becomes a problem for DecoyFinder, occasionally requiring users have to sacrifice decoy size for computational speed. First, the importance of accounting for net formal charge in decoy set generation highlighted by Irwin ( Irwin, 2008) and quantitatively assessed by Mysinger and Shoichet ( Mysinger and Shoichet, 2010) was ignored. However, DecoyFinder confronts three major limitations. To overcome this limitation, DecoyFinder was the first application developed to build target-specific decoy sets ( Cereto-Massague et al., 2012). DUD or DUD-E only contains limited decoys for a small set of targets. (2006) in 2006, and its enhanced version (DUD-E) was released in 2012 ( Mysinger et al., 2012). The first version was released by Huang et al. The Directory of Useful Decoys (DUD) was designed to meet this benchmarking need while controlling for decoy bias on enrichment. Thus, to compare ligand enrichments, a benchmarking set of actives and decoys is needed. Ligand enrichment measures how annotated actives rank versus a background of decoys. Ligand enrichment is a crucial metric for assessing the performance of molecular docking or other virtual screening methods. While molecular docking or other virtual screening methods are routinely applied in many drug discovery campaigns, quantitative assessment of their performance remains problematic ( Jain and Nicholls, 2008). Validation of the performance and efficiency of RADER was also conducted and is described. RADER provides two operational modes: as a command-line tool and on a web server. This program adopts a novel database-management regime that supports rapid and large-scale retrieval of decoys, enables high portability of databases, and provides multifaceted options for designing initial query templates from a large number of active ligands and generating subtle decoy sets. Here, we developed a program suite called RApid DEcoy Retriever (RADER) to facilitate the decoy-based assessment of virtual screening. However, desirable query template design, generation of multiple decoy sets of similar quality, and computational speed remain bottlenecks, particularly when the numbers of queried actives and retrieved decoys increases to hundreds or more. DecoyFinder was released to compensate the limitations of DUD or DUD-E for building target-specific decoy sets. The Directory of Useful Decoys (DUD) and its enhanced version (DUD-E) provide a benchmark for molecular docking, although they only contain a limited set of decoys for limited targets. Evaluation of the capacity for separating actives from challenging decoys is a crucial metric of performance related to molecular docking or a virtual screening workflow.