On the one hand, since EPIG aims to reduce predictive uncertainty in the most promising molecules, it might be inherently biased towards regions of the chemical space that are well-understood and well-represented in the training data. These regions are likely to contain molecules that are not only bioactive but also synthetically accessible and drug-like. On the other hand, the feedback provided by the noisy oracles on the selected molecules might implicitly consider factors like synthetic accessibility and drug-likeness in their evaluations. This feedback loop could gradually steer the model towards favoring molecules that score well on these practical metrics, even if they are not directly part of the initial scoring function. This could stem from the fact that the updated QSAR model might now use features related to drug-likeness and synthetic accessibility to predict bioactivity. Thus, when EPIG selects molecules to reduce uncertainty in bioactivity predictions, it might inadvertently select molecules with favorable synthetic accessibility and drug-likeness due to these underlying correlations. Despite uncertainty sampling resulting in higher performance on the MAE metric, EPIG's advantages in producing more drug-like and synthetically feasible molecules make it a compelling choice. These factors are crucial for real human-in-the-loop experiments, where the goal is not only to predict DRD2 activity accurately but also to discover drug-like candidates which are easy to synthetize. EPIG also appears robust to increased noise when simulating expert feedback, allowing for more efficient search for molecules with higher probabilities of being DRD2 active (Figure ). Therefore, we choose EPIG as the acquisition function for human experiments to Table Metrics calculated for the final selections of high-scoring molecules according to the DRD2 activity predictor (> 0.5), mono-objective optimization. For all metrics, we report the mean and standard deviation across 10 different replicates of each experimental run.