A problem that arises with data-driven generative models is that their capacity to generate molecules in drug discovery settings far exceeds the capacity for experimental validation. Faced with millions of possible molecules but only having a practical capacity of producing experimental results for thousands of molecules leads to the following research question: which selection process should a decision maker use to design a wet lab experiment, when presented with a large set of generated molecules and a limited experimental budget? We have previously constructed a framework for selecting a set of molecules of specified size for experimentation. With this project we aim to simulate a full drug discovery cycle of iterative experimentation. To our knowledge this is the first attempt at a multistep simulation of a Design-Make-Test-Analyze (DMTA) cycle incorporating suggestions made by molecular generation modeling.