In our exploration of multi-objective molecule generation, particularly targeting DRD2 bioactives with inactivity against hERG and high drug-likeness (QED), our focus was still on updating the DRD2 bioactivity predictor through active learning. In this multi-objective setting, EPIG and random sampling under highly noisy feedback emerged as the most effective strategies in terms of improving the generalizability of the DRD2 activity predictor and aligning its outputs with the oracle values, showcasing greater robustness and reliability (Figure ). ANOVA analysis indicated significant differences among acquisition strategies concerning their effectiveness in reducing the MAE for predicting DRD2 bioactivity (F (3, 36) = 13.49, p-value 8.40 × 10 -10 ). Posthoc Tukey's HSD tests revealed that EPIG (mean difference = -0.1101, p-value < 0.001), Greedy (mean difference = -0.0656, p-value = 0.008), Random (mean difference = -0.1311, p-value < 0.001), and Uncertainty sampling (mean difference = -0.0642, pvalue = 0.010) significantly improved MAE compared to the baseline. Among pairwise comparisons between acquisition strategies, only Greedy vs. Random (mean difference = -0.0655, p-value = 0.008) and Random vs. Uncertainty (mean difference = 0.0669, p-value = 0.006) showed statistically significant differences. No significant differences were observed between EPIG and Greedy (p-value = 0.154), EPIG and Random (pvalue = 0.815), EPIG and Uncertainty (p-value = 0.132), or Greedy and Uncertainty (p-value = 1.0). Fig. Generalization performance of the DRD2 activity predictor to molecules generated at each step of the process (using a multi-objective scoring function). We report the mean and standard deviation of the MAE between predicted and oracle LogP values across 10 different replicates of each experimental run. The start of the Active Learning loop at each round is illustrated by a star. The noise in simulated expert feedback increases from left to right.