Bayesian optimization of MSM hyperparameters is possible using tree Parzen estimators for the surrogate function. TPEs are able to easily model the different types of hyperparameters (continuous, integer and discrete) and improve the implied timescales. Multi-objective optimization can be used but does not give a clear advantage over optimizing a single objective. Caution must again be exercised as this can give rise to models in which the meaning of the eigenvectors can change.