Additional performance metrics, provided by the benchmarking platform MOSES and described in Appendix E, for the final selection (i.e., resulting from the final optimization step) of high-scoring molecules stored in memory are summarized in Table . We can observe that expert inputs do not significantly deteriorate the internal diversity of final molecular sets in comparison with the "No Feedback" baseline. Our results also demonstrate that both EPIG and random sampling significantly improve the exploitation-exploration trade-off, which can be observed by a maintained internal diversity score and an increased similarity between the generated molecules and those present in the initial training set when comparing with the "No Feedback" baseline. Moreover, based on the similarity metrics measured between the sets of generated molecules and the expert queries that were selected using each acquisition criterion, EPIG appears to identify the additional training molecules which are the most informative for improving the generalizability of predictions for the current most promising designs. Moreover, using AL with expert feedback to refine the LogP predictor leads to the generation of more synthetically accessible molecules in comparison with the "No Feedback" baseline and the random sampling strategy.