In general, SAMPL blind challenges typically involve a host-guest component that provides the community an opportunity to test and compare performance of a variety of computational methods on the same diverse data. The subsequent release of experimental data allows accuracy to be compared relative to experimental results which were not known when predictions were made, and the subsequent statistical assessment compares methods on equal footing. Upon evaluation, participants and organizers can assess the lessons learned and the potential value of different methods. Subsequently, computational methods and their algorithms can be calibrated and optimized for application in future blind challenges and in the real world .