Here are presented technical notes and tips on developing graph generative models for molecular design. This work stems from the development of GraphINVENT, a Python platform for graph-based molecular generation using graph neural networks. In this work, technical details that could be of interest to researchers developing their own molecular generative models are discussed, including strategies for designing new models. Advice on development and debugging tools which were helpful during code development is also provided. Finally, methods that were tested but which ultimately didn’t lead to promising results in the development of GraphINVENT are described here in the hope that this will help other researchers avoid pitfalls in development and instead focus their efforts on more promising strategies for graph-based molecular generation.