for selecting examples at each oracle iteration. Historically, AL frameworks have prioritized examples with the highest calculated uncertainty as being maximally informative for the model. Studies using AL for virtual screening have also used greedy acquisition strategies that select the examples (molecules) with the best-predicted score regardless of uncertainty. This approach successfully found the true top-scoring molecules from large libraries of compounds, a more useful metric for their purposes than model accuracy alone. Similarly, Gusev et al. combined similarity clustering with greedy cluster-head selection to train a relative binding free energy prediction model. By contrast, Thompson et al. found that the number of top compounds recovered was surprisingly indifferent to acquisition strategy, although all strategies outperformed random acquisition. We provide greedy, maximum uncertainty, and random orderings as options to the user, with maximum uncertainty as the default. 2. Getting the party started. The hit picking session can begin once the screen has started uploading and calculating interactions. The user either starts a new party by selecting the uploaded screen dataset or resumes an existing party to recover existing grades, models, and predictions from a prior session. Supporting Table provides all available settings and associated options.