Sampling multiple pathways can be conveniently achieved with adaptive-biasing approaches. These methods rely on sampling multiple pathways simultaneously, and thus can be expected to provide better convergence and smaller errors. In these methods, the state of the system is coupled to a system parameter, which is a function of the atomic coordinates. This parameter evolves simultaneously with the system following the distribution of the assigned probability weight factor -bias. The bias is directly related to the free energy landscape of the parameter. During the adaptive biasing simulations, the distribution of assigned weights is constantly updated based on the simulation history in order to achieve the target distribution, usually uniform, of the parameter. Indeed, we tested that for two of our systems, computing the PMF with an adaptive-biasing approach, namely, the accelerated weight histogram (AWH) method by Hess and co-workers, yields consistent and reproducible results. While, as we mentioned above, there are multiple different implementations of adaptive-biasing methods, including metadynamics, adaptive biasing force, and many others, only the AHW method is supported natively in GROMACS. We additionally demonstrate that using a shared bias between different simulations is essential. Thus, our results reveal that the widely accepted practice of performing US simulations does not lead to reproducible PMFs, and that instead, multiple pathways must be sampled simultaneously to avoid this problem and provide reliable free energy estimates. However, as methods that simultaneously sample multiple pathways are not yet fully established, further benchmarking will be needed to find the optimal parameters for routinely computing PMFs with those methods.