We use a random forest classifier ) as implemented in scikit-learn ) as a classification algorithm. The ratio of trees predicting that a compound is active is used as a scoring function. As features we use binary folded ECFP fingerprints of size 1024 and radius 2, which were calculated using rdkit . We obtained three classifiers for each of the three targets, JAK2, EGFR, and DRD2, with similar predictive performance (see Table ) suitable for goal-directed generation. For each model the performance was evaluated on the split that was not used for training. There is only one performance value for all three classifiers, because in expectation it does not depend on the used data split and random seed.