In computational chemistry and specifically in protein-targeted small molecule discovery, organizing benchmarking exercises similar to CASP has occurred , but none are currently operational. In addition, other than the TDT and DREAM benchmarking initiatives which included a prospective arm to its prediction challenge, there has been no concerted effort to provide experimental testing of predictions. This includes no opportunity to fund the synthesis and quality control of predicted compounds and to test their binding rigorously under standard conditions. Commercial sensitivities also complicate small molecule binding benchmarking. A large fraction of the experimental data suitable for benchmarking in silico binding predictions are generated within the pharma industry and kept confidential, rather than being released for general use. In addition, significant advances in computational chemistry technologies are taking place within companies, and massive private investment is flowing into new companies for the development of artificial intelligence (AI) methods. These companies are also likely reluctant to share their methods in any detail, or see them put to the test publicly. Finally, unlike the prediction of experimental protein structures, which is a narrowlyscoped problem with a relatively unambiguous solution, benchmarking the prediction of useful small molecules against a target is much more difficult -multiple possible solutions differ both quantitatively and qualitatively.