Across all three chemists, we observe an enhanced alignment between the mean predicted DRD2 bioactivity score and mean oracle score for the generated molecules throughout the process (Figure ). This indicates that inferring chemist feedback on DRD2 predictions and updating the generative agent leads to exploring different regions of the chemical space where molecules are more likely to be bioactive according to the oracle (Additional file 1: Figure ). This improved alignment is noted in comparison to scenarios where no feedback is solicited to update the DRD2 predictor, revealing a more pronounced gap between the predicted and oracle DRD2 bioactivities, and therefore the production of less optimal molecules. Regarding the enrichment of the top-scoring molecules in true positives, two out of the three chemist experiments resulted in improved enrichment compared to the "No Feedback" baseline (Figure ). The most notable improvement was observed when interacting with chemist 3. Both the mean oracle score of the generated molecules and the percentage of agreement with the predicted DRD2 bioactivities have increased as a result of updating the predictor based on chemist 3 feedback, suggesting that the general knowledge of this chemist about DRD2 bioactivity better aligns with the true likelihood of DRD2 bioactivity (Figure ). Consequently, inferring well-aligned feedback from chemist 3 leads to an improved understanding of the structure-activity relationship by the updated DRD2 predictor which in turn improves the task of designing new active candidates for DRD2. We also investigated how each chemist experienced this exercise after its completion. While the goal of acquiring feedback was to calibrate the DRD2 activity predictor and obtain molecules that are more likely to be active, chemists 1 and 2 had a different understanding. Chemist 1, who had never worked with the DRD2 target before and lacked specific knowledge about the structure-activity relationship, understood the instructions to assess how much they liked the molecule as a lead. Consequently, they aimed to select molecules that were synthesisable, stable, and with reasonable lipophilicity to maximize the chances of them being made and tested in a project. Similarly, chemist 2, who was also unfamiliar with the DRD2 dataset and binding site, did not fully grasp the intended focus on assessing the validity of the DRD2 activity predictions. We noticed that this misunderstanding stemmed from the question displayed through the GUI during these HITL experiments, which was "How much would you prioritize this molecule as a DRD2 binder?". This question did not clearly convey the emphasis on validating the DRD2 activity predictions. For the third HITL experiment, we reformulated the question to "How strongly do you agree with this molecule being predicted as a DRD2 binder?" (Figure ) to better align with the goal of obtaining feedback on the predictor's accuracy. Chemist 3 focused on known DRD2 actives to judge the plausibility of new proposed actives and also considered drug-likeness criteria. Unlike chemists 1 and 2, chemist 3 acknowledged a clear understanding of the goal, which explains why the third human experiment successfully led to the desired outcome. This highlights the importance of carefully designing user studies to enhance the efficiency of HITL approaches. Overall, all chemists found the interface easy to use and had a positive experience.