Table Metrics calculated for the final selections of high-scoring molecules according to the LogP predictor (Score > 0.5). For all metrics, we report the mean and standard deviation across 10 different replicates of each experimental run. Up and down arrows indicate the expected direction of improvement for each metric. One-sided ANOVA tests were applied for statistical significance assessments, and performance significance with respect to the "No Feedback" baseline is marked with * (if p-value < 0.05) or ** (if p-value < 0.01). Optimal frequency of human queries. The frequency of querying humans for feedback is another crucial factor influencing the performance of our AL approach. We tested different human querying frequencies (every 150, 250, 500 and 1000 generator optimization steps). Results are illustrated in (Additional file 1: Figure ). For updating regression models such as the LogP predictor, querying every 150 steps was found to be suboptimal, suggesting that the generator might not have adequate time to adapt from the feedback and converge to more optimal chemical spaces. Conversely, querying every 500 or 1000 steps may result in increasing the MAE between oracle and predicted values, indicating that less frequent updates can cause the predictor to not generalize well anymore to new explored regions of the chemical space. Through experimentation, querying every 250 steps emerged as the most optimal frequency. This balanced approach provided the model with sufficient time to integrate feedback and update its predictions effectively, ensuring better alignment with oracle values and improved overall generalization.