To address these questions, a huge amount of research has been focused into developing a broad spectrum of computational tools, ranging from fast and less accurate ones like docking and scoring methods to more computationally expensive, but ideally more accurate methods like relative/absolute binding free energy calculations . Computational methods should be regularly tested on blind data sets to prevent researchers from exaggerating the performance of their methods by optimizing or over-fitting their algorithms to reproduce known data sets and to ensure we understand the relative performance of new versus established methods in a predictive context. To this purpose, the Grand Challenge organized by the Drug Design Data Resource (D3R) community was conceived to provide a platform to evaluate methods in a blind way and also to compare methods from different research groups.