Tanimoto distance from known actives to promote diversity. Related work prioritized the compound with the highest predicted score per cluster. We primarily used Taylor-Butina clustering to quantify chemical diversity because it groups compounds with similar chemical structures, but there is no universally accepted way to assess the structural diversity within a compound set. Taylor-Butina clustering, which readily scales to large compound sets, is susceptible to the order in which the compounds are processed and can result in different clusterings. For example, an active may have been clustered earlier than an inactive that is within the cutoff distance. To avoid potential dependence of our diversity assessment on the choice of distance threshold used in Taylor-Butina clustering, we applied three different thresholds and observed consistent diversity differences between compound sets. The RF-C model outperformed the Similarity Baseline in both unique and novel cluster hits (Tables and), confirming it can prioritize novel chemical structures that are not present in the training data. Even though all of the compounds we ordered from Enamine belonged to different chemical clusters, some did contain the same core scaffold (Table ).