• We introduce a novel methodology for targeted molecule generation in the latent space of a pre-trained autoencoder architecture. The method combines powerful generative models pre-trained on massive chemical datasets with state-of-the art RL algorithms for continuous space optimization. • We discuss the properties of the late space that play a critical role in the introduced optimization framework.