To determine the hits for the 68 Enamine REAL compounds, we generated dose-response curves. We first applied the same hit assignment criteria as the AMS screen and identified 31 initial hits, a 45.6% hit rate (Figure ). The training set had a 0.13% hit rate, so a random selection of 68 compounds would be expected to yield no hits. We intentionally selected top scoring compounds from different clusters in the prioritized Enamine set, which were also distinct from clusters with active training or AMS compounds, so that the 31 active compounds each represent a unique cluster by design. Similar to the AMS screen, the distributions of the Tanimoto distances to the most similar known active of the Enamine active and inactive compounds are not substantially different (Figure ). When we examined the full dose-response curves, we confirmed 28 compounds as hits with IC50 values ranging from 1.3 to 37.8 µM (Table ). Half of these hits had IC50 values of 10.0 µM or less. To check for potential false positive hits, we applied computational filters that assess chemical structures that may interfere with the AlphaScreen assay or be histidine mimetics or nickel chelators. 17 of the 28 hits were flagged with these filters (Table ). Although the filters suggest caution when interpreting these particular hits, they do not guarantee the hits are false positives. Structure-based filters are imperfect. Many inactive compounds also matched the filters -6 from Enamine (Table ), 149 from AMS, and 3,281 from the training dataset -even though these could not have interfered with the assay. Even though the RF-C model was only given a budget of 68 compounds to explore the billion compound Enamine library, it identified tens of active compounds. These active compounds share common core substructures with their nearest active compounds in the training data and AMS screen. However, they frequently depart significantly with respect to their peripheral substituents ( and a common source of prediction error. However, these 40 Enamine inactives generally are less similar to the active training instances than compound pairs that would be considered to form activity cliffs.