We coupled CSLVAE with a normalizing flow reinforcement learning agent that learns to sample from a distribution over the CSL induced by a reward function representing desirable molecular properties. When there are few or no known compounds interacting with a therapeutic target, obtaining a diverse set of hits is critical for subsequent development. Therefore, the policy is specifically designed for efficient exploration and exploitation, avoiding mode concentration (Methods). Because our framework utilizes neural network based generative models to search over synthetically accessible chemical spaces, we call the approach NeuralGenThesis (NGT, Figure ).