A probabilistic model of the chemist's score can estimate the unknown parameters of the desirability functions sufficiently well from the feedback, and as a result, the adapted MPO scoring function achieves improved QED score in the generated molecules after just one round and 100 HITL interaction. The uncertainty in the model decreases after feedback (Supplementary material Section 3.1), and as a result, the error in the estimated MPO parameters decreases with increasing feedback, shown in Figure . The adapted scoring function also improves the quality of the generated molecules at each round, as seen in the increase in the average oracle score in Figure , which is the main objective of the method. All query selection strategies are effective in increasing the performance, but Thompson sampling achieves the highest average score and least variation in repeated experiments. In addition, random sampling performs relatively well overall, which often is the case in high-dimensional problems. Pure exploitation strategy leads to high variation in performance, which means that sometimes the method is very efficient, likely when the initial model is not biased; on the other hand, this greedy exploitation strategy is sensitive to errors in the early stages of interaction and cannot recover because it does not explore. In contrast, uncertainty sampling suffers from exploring too much, compared to Thompson sampling that balances the exploration and exploitation.