There are dozens of parameters whose values may affect the quality of the docking models produced by DockOpt, but a few tend to have the most impact. These include the thickness of the layer of low dielectric and ligand desolvation regions in the binding site, which affect the electrostatic and the ligand desolvation scores, respectively 52 . Other important parameters include the number and position of orientation spheres (also known as "hotspots" or "matching spheres"), which affect how ligand poses are sampled in the binding site . Still other significant parameters include the target number of poses to generate (match_goal), how overlap between ligand and protein is treated (bump_maximum and bump_rigid), and whether conformations are biased for compatible matches during sampling (chemical_matching) . DockOpt performs retrospective docking on multiple docking models in parallel using a job scheduler, such as Slurm or SGE. Although these two schedulers are the only ones currently supported, it should be straightforward to incorporate any queueing system into DockOpt. After docking, the docking models are evaluated by the specified criterion (e.g., normalized LogAUC) and then ranked by their performance. Depending on the user's specification of the configuration file controlling the program, the optimization process may repeat until the stopping criterion is met. A report in HTML format of the best parameter set choices is generated, together with figures summarizing all candidate models tested (see Methods, and below). A CSV file of their performance is saved as results.csv. The sets of "dockfiles" for the parameterizations found to perform best are saved in a dedicated directory best_retrodock_jobs/. Dockfiles for all other parameterizations can be found in the directory working/, as indexed in results.csv.