The ideal virtual screening algorithm would not only prioritize many active compounds but also identify chemically-diverse or structurally-novel hits. Clustering compounds by chemical structure provides one way to assess chemical diversity. We clustered the union of the training set of 427,300 compounds and the 1,024 AMS compounds with a custom implementation of the Taylor-Butina method using Tanimoto distances between the compounds' fingerprint representations, the same chemical features used by the models. Tanimoto distance is defined as 1 -Tanimoto similarity. We defined two chemical diversity metrics: unique cluster hits and novel cluster hits. We identified the clusters with at least one AMS hit regardless of whether or not these clusters contain training set compounds. This cluster count is defined as the unique clusters hits metric, which gives a measure of the hit diversity. In addition, we calculated the novel cluster hits metric, which is the number of clusters that contain AMS hits but do not contain any training set hits. Novel cluster hits measures how well a model was able to generalize to active chemotypes that were either unexplored or showed no activity in the training dataset. This is similar to an existing novelty measure. Table summarizes these cluster hit metrics at a clustering distance threshold of 0.4 (see Table for other thresholds). The RF-C model substantially outperforms the Similarity Baseline in both hit diversity metrics, finding twice as many novel chemical clusters. There was an association between the clusters defined using chemical structures and a chemical's % inhibition. We categorized compounds as weak, moderate, or strong actives based on % inhibition ranges (Appendix B.2 and Table ) and counted the number of weak, moderate, and strong actives in each cluster. The different categories of active compounds did not distribute uniformly across the chemical clusters (Fisher's exact test p-value 0.0005).