We follow the same procedure to investigate the results from the generation of DRD2 bioactive molecules based on the DRD2 bioactivity predictor. We observe that, in comparison with the "No Feedback" baseline, all acquisition strategies resulted in improved alignment between predicted and oracle DRD2 values (measured in terms of MAE between predicted and oracle probabilities of being DRD2 active) even under the presence of noisy feedback (Figure ). Among these strategies, uncertainty sampling emerges as the best performing one in terms of MAE reduction, outperforming EPIG, greedy, and random sampling strategies. Statistical analysis using the Tukey HSD test indicated significant differences between the "No Feedback" baseline and all acquisition strategies (ANOVA, F (3, 36) = 19.87, p-value < 1.85 × 10 -12 ). Specifically, all acquisition methods significantly improved MAE compared to the baseline (p-value < 0.001), with uncertainty sampling showing the most substantial improvement. However, no significant difference was observed between uncertainty sampling and EPIG (mean difference = -0.1094, p-value = 0.0723), suggesting similar effectiveness in enhancing the DRD2 bioactivity predictor's generalizability. The comparison between EPIG and greedy sampling yielded a mean difference of 0.0524 with a p-value of 0.717, indicating no statistically significant difference. Similarly, the comparison between EPIG and random sampling methods resulted in a mean difference of -0.0062 with a p-value of 0.9999, indicating no statistically significant difference.