Between then and now there has been an explosion of interest in machine learning and AI methods in drug discovery . While these new methods had the potential to be gamechanging, there is the ever-present challenge in this sector of hype, in the sense that the actual capabilities of some of the newer technologies, outside of marketing statements, are sometimes not clear. In OSM the openness extends to the research process itself, allowing contributors to share what they are doing, rather than what they have done. The use of competitions to progress scientific research is not novel in itself, with previous examples of this in data analysis for drug discovery , but it is uncommon for competitions to be accompanied by the next crucial step: benchmarking by chemical synthesis and biological evaluation of predicted molecules. It is rarer still for science competitions to run completely openly, where everyone can see, and potentially incorporate, other entrants' solutions as they are submitted. We felt we could achieve two things by running this competition with OSM's open source ethos, in which those submitting entries would reveal their predictions in real time and, ideally, provide full methods (within the boundaries of commercial sensitivities). We would be able to approach the scientific problem along multiple paths, but we would also be able to provide a clear case study of the current effectiveness of predictive modelling in phenotypic drug discovery.