Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models. Here, we propose a graph-based deep generative model for de novo molecular design using reinforcement learning. We demonstrate how the reinforcement learning framework can successfully fine-tune the generative model towards molecules with various desired sets of properties, even when few molecules have the goal attributes initially. We explored the following tasks: decreasing/increasing the size of generated molecules, increasing their drug-likeness, and increasing protein-binding activity. Using our model, we are able to generate 95% predicted active compounds for a common benchmarking task, outperforming previously reported methods on this metric.