Our comparison of the four acquisition strategies revealed similar performance for EPIG, uncertainty and greedy sampling in improving the generalizability of the LogP predictor in terms of significantly reducing the MAE between estimated and oracle values, with greater performance than a random sampling baseline and a slightly higher performance when using model uncertainty as a criterion for data acquisition, followed by EPIG. To statistically validate these findings, we conducted an ANOVA test, which indicated significant differences across the acquisition strategies (F-statistic = 16.63, p-value < 1 × 10 -10 ). Further pairwise comparisons using Tukey HSD test revealed several significant differences between the strategies. Specifically, EPIG, uncertainty, and greedy sampling methods significantly outperformed the "No Feedback" baseline in terms of mean LogP predictor accuracy, with p-values < 5 × 10 -4 . EPIG also significantly improved performance over random sampling (p-value = 2 × 10 -4 ), while no significant differences were observed between EPIG, uncertainty, and greedy sampling themselves. These statistical analyses confirm that the choice of acquisition strategy has a substantial impact on the improvement of the model accuracy, with EPIG and model uncertainty criteria showing the most promise.