Because measures of enrichment capacity do not incorporate any structural information of predicted poses, the use of normalized LogAUC as the single criterion for model evaluation means that there is the possibility of the model overfitting to the ligands provided by the user, which usually number at most a few dozen. However, this potential issue could be ameliorated by using an alternate criterion measuring pose reproduction, or perhaps even a criterion measuring both enrichment and pose reproduction. Until such evaluation criteria are implemented in DockOpt, users are advised to provide as many diverse known ligands as possible to mitigate the risk of overfitting. Future versions of DockOpt could incorporate the use of a validation set to more rigorously address the potential issue of overfitting. This set would consist of ligands not included in the training data, thereby serving as an independent measure for evaluating the model's generalization performance. Such an approach would provide a more robust metric for assessing the efficacy of the model in predicting poses and ligand enrichment for unseen compounds. These caveats should not obscure the main results of this work. We have developed a fully automated tool for the creation, evaluation, and optimization of docking models, which is now available as part of the UCSF DOCK 3.8 distribution. In addition to being offered free of charge to academic researchers via , the software can also be used for free by non-academic researchers upon registration at . It is important to note that we cannot guarantee the results of any docking screen using DockOpt; this software is to be utilized at the user's own risk. For best outcomes, we strongly encourage the use of sanity checks and controls at every stage of the docking process, as discussed throughout this work.