We adopt MolGAN's reward network to heuristically bias generation towards high scoring molecules, while avoiding the mode collapse issues commonly seen with GANs by using a compound normalizing flow architecture in our generator allowing us to train MoFlowGAN on three objectives: likelihood maximization, adversarial loss minimization, and reward optimization. MoFlowGAN is, to our knowledge, the first hybrid FlowGAN model for graph-structured data.