The final human inspection stage is a less studied rate-limiting step, wherein computational medicinal chemists manually evaluate predicted structures. A review of drug discovery protocols found visual inspection of docked poses in 50% of the 250 publications surveyed, often as the final step in compound prioritization before experimental testing. Multiple scientists usually collaborate in visual inspection, working toward a consensus during "Hit Picking Parties." It has been suggested that any sufficiently experienced human could outperform existing scoring functions for compound selection. Indeed, studies have found molecules chosen by expert computational medicinal chemists achieve greater potencies than those selected by scoring functions alone. However, there are limits to the number of poses a single scientist can realistically examine. This is especially troubling given recent reports of a hit-rate plateau burying many false negatives further down in the screening results. Moreover, despite efforts to codify rules for visual inspection, there remains no high-quality database of expert annotations for learners to use as a reference. This problem is further complicated by the fact that even expert medicinal chemists often disagree with one another. In this application note, we introduce Autoparty, a tool for AI-assisted accelerated hit picking on molecular docking results. Autoparty is a browser-based tool for visualizing the protein-ligand interface and intermolecular interactions. The user assigns grades to each molecule, which are stored in a database and available for export. Autoparty uses active learning with human-in-the-loop training to display the most useful poses for the user to annotate. This trained model can then be applied further down in the docking screen to rescue false negatives that would not be found by standard inspection procedure. We hope this tool will bring together ML advancements with medicinal chemical expertise to facilitate pose evaluation and recovery of otherwise hidden promising compounds.