Weighted ensemble (WE) methods Weighted Ensemble methods are a powerful approach for rare event sampling that divides the system's configuration space into regions or Chapter bins, assigning weights to trajectories based on their relative probability of occurring . Each bin is populated with multiple trajectories, which are propagated independently, and the weights are adjusted after each iteration to maintain an accurate representation of the probability distribution across the system. This approach allows for efficient sampling of rare events by allocating more computational resources to low-probability transitions. WE methods excel in parallelization to some extent, as independent trajectories can be run in parallel, with the results being integrated periodically to update the weights . However, a key challenge with WE methods is that each trajectory cannot be easily run asynchronously; they must synchronize periodically to redistribute weights, which can make parallel execution less straightforward compared to methods where trajectories are fully independent. If the bins or weights are not properly chosen, the sampling efficiency can be compromised, and convergence checks are necessary to ensure the results accurately represent the system's equilibrium dynamics. Nevertheless, one of the key strengths of WE methods is their ability to enhance the sampling of rare transitions while still maintaining detailed dynamical information about the system. Indeed, multiple studies of rare events and long-timescale processes, including drug unbinding, have been conducted using weighted ensemble methods on HPC systems, highlighting their powerful ability to model compelexes .