Our experiments aim to showcase the benefits of integrating human feedback through AL to refine target property predictors, compared to no predictor refinement (i.e., the predictor remains static throughout R rounds of the REINVENT loop). The improvement is measured in terms of error reduction between the predicted scores and the oracle scores associated with the generated molecules that the predictor identifies as promising for satisfying the target property. A diminishing error over R generation cycles indicates an enhanced predictor generalization to new molecules from REINVENT . This means that molecules identified as promising by the predictor are also promising according to the oracle. Consequently, optimizing a target property predictor that generalizes well outside its training domain results in generating more molecules that satisfy the target property.