The use of Markov State Modelling enables to decrease the time-to-answer by modelling long-time scale dynamics as a set of shorter timescale simulations that may be run concurrently. However, obtaining equilibrium distributions require the use of dimensionality reduction. The MD data is reduced to chosen features, which in turn are clustered into discrete microstates. Those discrete states in this case were assigned to final "active" and "inactive" PTP1B states using Perron Cluster-Cluster Analysis (PCCA), which uses the eigenvectors of the transition matrix that makes up the MSM to find metastable states . Tools that make this procedure simpler and data-driven, such as VAMPnets are in development. The procedure is more complex when comparing multiple MSMs, rather than focusing on a single model. It is preferable that both the microstates and the coarser active/inactive assignments are based on the same feature values for the models to be more easily comparable. Additionally, to determine the active and inactive state partition, the assignments from PCCA of the reference system were used throughout to keep them consistent. However, seven total MSMs were built in this case, and any of those assignments could be used. Supplementary Figure shows the effects of using different active state definitions on the active state probability and ranking. The results remain qualitatively consistent but in general the automated selection of a suitable macrostate definition is non-trivial. Therefore future work focusing on automating the MSM construction and analysis steps is desirable to facilitate deployment of the technology at scale.