A major bottleneck of SMA-MD remains the computational cost per sample in comparison to MD. However, as previously discussed, while MD is intrinsically sequential, the SMA-MD framework is fully parallelizable (except for the short individual simulations) and therefore allows for an efficient way to use modern computing hardware, including GPUs. Indeed, this divide-and-conquer strategy is successfully applied in the Markov state modeling community . Nevertheless, SMA-MD, in the context presented here does not outperform RE in terms of runtime, see Appendix 9.14. This limitation is related to the high cost of sampling from the generative model and the low sample efficiency. Due to continual improvements in the field of DGMs, we believe both of these issues will be resolved in the near future.