The unbinding process is composed of multiple barriers where the ligand may disrupt/form key interactions and overcome internal torsional barriers before reaching the solvent. With the process of learning the CVs from RAMD, we hope to partition the conformational space in a way that minimizes overlap of the bound, transition, and unbound states, which is paramount for iMetaD accuracy. The violation of this assumption may be observed in retrospective validation as it would result in systematically overestimating the residence time. This is due to sampling orthogonal degrees of freedom on an unbiased timescale instead of accelerating them. In all the iMetaD runs we employed well-tempered metadynamics with an initial deposition rate of 0.2 kcal/mol every 8 ps and a bias factor of 10. Additionally, since the free energy roughness is difficult to predict, and is likely to be system-dependent, we adopted diffusion-based flexible hills with an interval of 16 ps. Due to the stochastic nature of the process, 20 independent iMetaD replicas were run using the path CVs from the main egress path and the residence time was estimated by fitting the cumulative distribution function to an exponential distribution as detailed in Salvalaglio et al using bootstrapping for error estimation.