In the rapidly evolving field of drug discovery, the integration of generative AI with a physics-based active learning framework is proving to be a game-changer. This innovative approach leverages generative models (GMs) to design molecules with specific properties, addressing common challenges such as target engagement and synthetic accessibility. By merging a variational autoencoder with nested active learning cycles, researchers can iteratively refine predictions, leading to the generation of diverse, drug-like molecules that exhibit high predicted affinity and synthesis accessibility.