and f d (k) = k/n, leading to a linear flow distribution with constant transition probabilities. In principle, in a HREM simulation we should try to adapt the scaling ladder to reach such a linear regime for the flux. Flow optimization in HREM is prohibitively costly as it requires long adaptive enhanced sampling simulations to estimate to flux, since many visits to the state k are necessary to get a statistically reliable f u (k). An alternative less demanding estimate is evaluated, rather than directly using the slowly convergent Eq. 2, using the approach described in Ref. based on the mean first passage times (MFPT) within the scaling factor ladder (see Eq. 6-11 of Ref. ). In the latest ORAC distribution, we provide a simple bash script, replica flux.bash, to compute the flux in HREM using the MFPT approach proposed by Nadler et al.. While flow optimization, whether using Eq. 2 or the estimate based on the MFPT, is costly, balancing of the acceptance ratio P acc can be achieved rather quickly with adaptive HREM scaling. Balanced P acc avoids any possible "bottleneck" in the HREM simulation. Bottlenecks may significantly slow down convergence and give rise, in the worst case scenario, to regions of non communicating replicas due, e.g, to some sharp transition as a function of the scaled potential, typical of rare events (e.g. transition between torsional-driven conformational states).