The SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) challenges focus efforts on improving and advancing computational methods through crowdsourcing. Blind challenges, like SAMPL and companion challenges such the the Drug Design Data Resource (D3R) Grand Challenges, ensure participants do not know experimental values when running calculations , ensuring that method comparisons are fair and performance is hopefully indicative of what could be expected in real-world applications to related problems. Host-guest systems form the basis of one category of the SAMPL challenges (with others focusing on predicting physical properties, and on protein-ligand binding) and typical challenge performance indicates such host-guest systems still pose challenges to contemporary methods . Occasionally, host(s) or guest(s) are revisited, so related experimental results are available, but we avoid cases where the experimental value being predicted is already available in the literature. A wealth of experimental data is already available, so SAMPL focuses on predictive tests rather than retrospective analysis. The SAMPL challenges are organized in this manner to ensure no participant, even accidentally, adjusts their method to agree with "correct" values thereby introducing bias. For example, when experimental values are known, a naive participant could stop calculations when they agree with the experimental value because they have "converged". Or more subtly, a participant could run calculations with several different sets of settings in the simulation package used and conclude that the settings which gave the best results were optimal, whereas in fact they might be just observing random fluctuations. Blind challenges avoid such opportunities for bias.