With the advent of Deep Generative Models (DGMs) , a family of new methods to generate unbiased one-shot equilibrium samples of the Boltzmann distribution were proposed under the name of Boltzmann Generators (BG) . These methods approximate the Boltzmann distribution of a molecular system with a DGM which allows efficient sampling and exact likelihood evaluation, commonly Normalizing Flows . Efficient sampling allows to side-step iterative simulation methods, and exact likelihood evaluation allows to recover unbiased samples through importance sampling or importance weighing . Previous work has successfully used BGs to sample from large molecules such as proteins or solids . However, these approaches currently do not generalize to different molecular systems, and designing models that are transferable across different molecules remains a challenging task. Another family of recent related methods, such as Implicit Transfer Operator Learning (ITO) or Timewarp , tackles the sampling problem by modeling the generative process in Molecular Dynamics simulations, yet on much longer time-scales. Nevertheless, currently, these approaches similarly suffer from limited transferability.