By acknowledging and actively addressing these limitations, we can foster a more comprehensive understanding of the scope and applicability of our approach in realworld drug discovery settings. This will ultimately maximize the potential benefits of human-machine collaboration in advancing drug discovery efforts. Figure illustrates the predictive performance of the pre-trained penalized LogP predictor with respect to D 0 . Figure is a screenshot of the GUI "Explanation" window displaying visual explanations of the individual DRD2 bioactivity predictions for the selected molecules via the EPIG acquisition strategy. Figure is a screenshot of the GUI "Similar Actives" window that displays the most similar active molecules already available in the initial training set of the DRD2 predictor for each of the selected molecules via the EPIG acquisition strategy. For the penalized LogP optimization use case: Figure shows that, except for uniform random sampling, increasing the number of expert queries (from 10 to 20 then to 30) selected using different data acquisition criteria does not have a significant impact on the predictor performance improvement, Figure shows that a higher frequency of expert queries (i.e., querying the expert every 100 steps of REINVENT instead of 250) results in slower predictor performance improvement, while lowering the querying frequency (i.e., querying the expert every 500 or 1000 steps of REINVENT ) leads to higher predictor error from the start and therefore slower performance improvement through HITL-AL. For the DRD2 activity optimization use case: Figure shows that, for greedy and random sampling, increasing the number of expert queries can result in higher predictor performance improvement through HITL-AL, Figure shows that a higher frequency of expert queries results in slower predictor performance improvement, while lowering the querying frequency can fasten convergence to low MAE. Figures and show how the HITL-AL approach compares to non-AL approaches such as train set similarity optimization and transfer learning. Figure shows the distribution of high-scoring molecules generated during a multi-objective molecule generation with (in gray) and without (in red) intervention of chemist experts. Table summarizes dataset specifications used for QSAR modelling and predictive performance assessment on holdout test sets, prior to deployment for molecule generation with REINVENT . capture the abstract notion of esthetics in medicinal chemistry .