BLUES simulations, we chose a lag time of 200ps to discretize the simulation data as this corresponds to the amount of simulation time between each move proposal, where a change in binding mode may occur. We applied TICA to extract the slow order parameters from our feature set (ligand heavy atom coordinates). From our TICA coordinates, we cluster our simulation frames into discrete microstates using k-means clustering. This gives us a set of N discrete microstates (i.e. simulation frames) which we map into the coordinate space of the first 2 TICA components (Fig. ), where N is defined as the square root of the total number of simulation frames (the default setting for PyEMMA).