For instance, RL was employed to optimize a pre-trained Recurrent Neural Network (RNN) to generate binders for the Dopamine Receptor D2 (DRD2) based on QSAR predictions . However, optimizing such predictors for molecule generation faces challenges as they often struggle to generalize post-deployment due to limited training data and evolving distributions during optimization . Consequently, generative NNs guided by such predictors may yield sub-optimal molecules by overly relying on predictions in poorly understood chemical space regions . Our work addresses this particular challenge.