To use a variational score, it is necessary to specify the lag time (τ ) of the MSM and the number of slow relaxation modes to optimize (k), and then estimate the MSM with different hyperparameters. The 'best' set of hyperparameters is the one with the highest variational score. In the case of the VAMP-E score, one may also add k to the list of hyperparameters to optimize. This procedure removes the need for potentially arbitrary hyperparameter selection with the concomitant risk of findings that are not robust to changes in modeling assumptions. This method has been used in a number of different studies. In addition, it has allowed investigations into the roles of various hyperparameters and for other methods for hyperparameter selection to be developed. In Husic et al. the authors used the GMRQ to show that the Ward and K-means methods are optimal for clustering conformations for MSMs. In Husic et al. the authors performed a sensitivity analysis of the GMRQ in order to determine the sensitivity of hyperparameters in describing protein folding. An extension of the VAMP score by Scherer et al. showed that the optimal set of features could be selected before going through the full MSM creation and scoring pipeline.