Supervised learning is used to extend D K f,exp and D K c,exp datasets to cover all 270 possible inhibitors in order to provide measurements for the full Cartesian product space. We first train a classifier on D K c,exp which makes synthesizability predictions for the remaining 174 candi-dates. This procedure produces a hybrid dataset D K f,hyb (containing both experimental and virtual/predicted synthetic feasibilities) which is ultimately used as the a priori unknown constraint oracle in our optimization experiments. Next, a regressor is trained on D K f,exp and predicts the IC 50 values for the structures deemed synthesizeable by our classifier. The NGBoost algorithm is used for both classification and regression. Additional details on this procedure, along with the cross-validated performance of our supervised learning models, can be found in SI Sec. S. sizeability constraints. The results of the experiments are shown in Fig. and tabulated in Tab. II. Once again, we examine the percentage of the parameter domain traversed in order to find the inhibitor with the smallest IC 50 in the dataset, and the percentage of in-feasible measurements incurred along the way. Template and alkyne structures are represented to the optimizer using hand-selected physicochemical descriptors accessed via the Mordred Python library. Additional details on descriptor generation can be found in SI Sec. S. 1 C 4.