The use of DockStream as a stand-alone tool was demonstrated by benchmarking the DEKOIS 2.0 dataset which curates 81 targets each with 40 actives and 1200 decoys. The dataset was curated to represent a reasonable fair assessment of docking performance by addressing diverse properties, including removal of Pan Assay Interference Compounds (PAINS) and covalent binders in bioactivity data, ensuring similar physico-chemical property matching between actives and decoys, and ensuring diverse chemotypes to avoid scaffold bias. The overall docking results reproduce and extend the observations of the original DEKOIS 2.0 benchmarking. The 5 supported docking backends: AutoDock Vina, GOLD, Glide, Hybrid, and rDock were paired with 3 ligand embedders: Corina with TautEnum, LigPrep, and RDKit with TautEnum to generate 15 distinct docking configurations. It is important to note that each docking configuration has many tunable parameters and screening every permutation is infeasible. Therefore, the default settings were kept with a few exceptions (see Methods for details). The benchmarking script was used to automate docking using each docking configuration for all 81 targets, resulting in 1215 docking runs and over 1,500,000 ligands docked. The analysis script was used to automate enrichment analysis and the pROC AUC values are shown in Fig. . The pROC AUC for a docking configuration that is no better than random selection in distinguishing actives and decoys is 0.434. Glide led to the most observed enrichment followed by Hybrid and GOLD, while the open-source docking backends, AutoDock