Community-wide blinded prediction challenges are a powerful and rigorous way to evaluate computational approaches to a range of technical problems. They allow a direct comparison of methods, because all participating groups work on the same systems or data, and they avoid bias that might otherwise result from the availability of the "true" result to the predictors. Accordingly, blinded challenges are used by multiple communities, including protein structure prediction (CASP(1,2)), protein function prediction (CAFA(3)), various problems in machine learning (Kaggle), and protein-ligand pose and affinity prediction (D3R(4)). The SAMPL blinded prediction exercises challenge computational chemists to predict various experimental observables related to computer-aided drug design , posing problems that range from full-on prediction of protein-ligand binding affinities down to the prediction of much simpler physical properties, such as the hydration free energies of small molecules.