However, the improvement in terms of drug-likeness (QED) and synthetic accessibility is less notable and not statistically significant in comparison with the "No Feedback" baseline (at the exception of the greedy sampling strategy which results in significantly higher mean QED score). Maintaining a sufficient level of internal diversity as well as drug-likeness and synthetic accessibility within generated molecules is crucial in multi-objective optimization settings where the balance between generating DRD2 actives and useful molecules simultaneously is essential. EPIG also showed potential in balancing exploration (i.e., diversity of generated molecules) and exploitation (i.e., similarity between generated and training molecules), as well as a good ability to retrieve the most informative additional training molecules to update the model, which is evidenced by the similarity metrics between generated molecules and the selected queries to be evaluated by the expert. Based on the simulated experiment results, we use EPIG as the data acquisition method of choice for the following human experiments.