Computational tools have recently improved at decoding the complex conformational and compositional heterogeneity signal from noise. In cryoEM, human intervention or machine-learning tools can distinguish different large conformations. While many of these tools are primarily used for visualization, some can incorporate discrete states into heterogeneous refinement, moving towards ensemble-based CyroEM models . In X-ray crystallography and cryoEM, methods exist that automatically detect subtle conformational shifts, like rotamer jumps, among structural ensemble members through multiconformer approaches . Further, weak signals representing compositional heterogeneity, often seen in X-ray ligand soaking experiments, can now be more easily identified using approaches such as PanDDA . In cryoEM, compositional heterogeneity often occurs by having subsets of a complex subunits on the grid throughout the data collection. This can been explored by exploring differences in the same or related maps However, these tools are confined by the data structure that must represent their output in the Protein Data Bank. Failing to account for the diverse conformational and compositional states hinders a thorough understanding of biological functions, the precision of predictive modeling, and the innovation in designing novel proteins and small molecule inhibitors.