Deep generative models are powerful research tools for de novo drug design. Throughout the training process these models can construct smooth latent space embeddings. Once fully trained the learned latent spaces can be explored, and information-rich clusters can be identified. Such continuous latent spaces allow users to sample and decode novel molecules with desirable features.