Three themes emerge from this study. First, a new automated pipeline for docking model creation, evaluation, and optimization has been developed. Second, the automated procedure can optimize the docking parameters significantly better than our previous automated system, and for most targets produces docking models that are suitable for large-scale prospective screens. Third, the new pipeline can be installed locally or accessed via a new web interface we have created, and the resultant docking model may be downloaded and deployed for large-scale docking, either on-premises or in the cloud . We take up each of these themes in turn. DockOpt implements many of the best practices in our standard lab protocol 26 in an automated fashion. We have augmented this procedure to include optimization techniques that capture many, but not all, of the current best practices in our lab. For example, the boundaries of the regions of low dielectric and ligand desolvation in the binding site are optimized. Work in our lab suggests that defining these boundaries is often critical to obtaining satisfactory retrospective enrichment during model optimization . The pipeline also optimizes the matching spheres used for sampling ligand orientations, which play a key role in determining whether a docking model is likely to succeed in a prospective screen. Furthermore, the software has been designed with flexibility in mind, allowing for the optimization of dozens of additional parameters, and minimal development effort is required to incorporate and optimize any new parameters or search strategies that a researcher might consider desirable. DockOpt demonstrates competent docking performance against the DUDE-Z benchmark regularly used in our research. In 36 out of the 43 DUDE-Z systems (~84%), this fully automated procedure with beam search produces a docking model exhibiting a normalized LogAUC of at least 15%. This threshold is a good heuristic for whether a docking model is suitable for prospective docking. This performance represents a striking improvement compared to the human-made docking models published in DUDE-Z, which resulted in only 13 successes (~30%) by the same standard.