An example of using Bayes to "generate hypotheses" that's successful is the mining/oil industry that makes spatial models and computes posterior expected reward for different drilling plans. For general-science type hypotheses you'd ideally want to put a prior on a potentially very complicated space (e.g. the space of all programs that compute the set of interesting combinations of reagents, in your example) and that typically isn't attempted with modern algorithms. This isn't to say there isn't room to make improvements on the state of the art with more mundane approaches.