An important aspect of this challenge was its emphasis on the exploration of billions of candidate compounds for activity against the target proteins. This deviates from a more common strategy of focusing on either known drugs (e.g. DrugBank , DrugCentral ) or bio-like molecules (e.g. ChEMBL , SWEETLEAD , GEOM ) in that it explores a massive space of synthesizable molecules that may bear little recognized similarity to known bioactive compounds. While known drugs carry the benefit of faster path to clinical distribution, and bio-like molecules are generally perceived as being more likely to successfully translate to clinical relevance, there is reason to expect that exploration of a much larger set of candidates may yield drugs that are unlike others identified previously. For example, Lyu et al. observe that billion-scale libraries are dramatically diminished for bio-like molecules relative to more focused libraries, yet still contain many experimentally-confirmed actives, as well as thousands of high-ranking molecules in docking assays. This observation justifies continued emphasis on development of methods for computationally screening billion-scale libraries. We also note that de novo generation of candidate molecules may offer a viable path to discovery.