The challenge of sampling alternative states has gained much traction with the recent surge of machine learning-based methods. These approaches aim to interpolate distributions of known structures to predict alternative conformations of a protein. Broadly, they can be categorized into two main groups. The first group consists of methods that train end-to-end models to directly output structures. For example, models like CFold and Distributional Graphormer (DiG) can directly generate alternative structures, such as holo-like apo conformations. Other examples in this group are co-folding models like AlphaFold3, 10 its various implementations, RoseTTAFold All-Atom, and NeuralPlexer. They generate structures in complex with their chemical or nucleic acid substrates, where the protein part of the structure needs to adapt to the various substrates. The second group of methods modifies existing structural generation models (often AlphaFold2) and extracts information from them to identify alternative structures. Notably, it has been shown that subsampling Al-phaFold2 with reduced multiple sequence alignment (MSA) depth can yield multiple states of the same protein. Several methods employ reduced MSA AlphaFold2 (rMSA AF2) as a hypothesis generator and analyze the resulting prediction ensembles to identify or enrich meaningful alternative conformations. Others modify or customize MSA information to gain diversity in the generated structures. Models that solely depend on conformation statistics or coevolution information come with an inherent shortcoming: the lack of physical information. It is an open question if co-folding models learn actual physical interactions, or simply learn patterns in the training set, or even worse, memorize the training set. The challenge to incorporate physical information is also faced with these methods tweaking the MSA for structural diversity.