The most prominent link between conformational entropy and structural ensembles is inferred from NMR order parameters. However, there is ambiguity in the structural elements that give rise to a set of order parameters, as outlined in Figure . One way to resolve this ambiguity is to use experimental data as a bias in MD simulations . This approach has provided insights into how mutations, counterintuitively, stabilize Chymotrypsin Inhibitor 2 through increased side chain dynamics in the hydrophobic core . The use of experimental biases in simulation is currently distinct from improved modeling and refinement methods that create structural ensemble models in X-ray crystallography or cryoEM . Once refined, these models can simultaneously extract conformational entropy and structural interpretations 10 , unhindered by the timescale constraints in NMR and MD simulations. Integrating multiple datasets simultaneously, particularly time-resolved 78 or fragment-soaking experiments , has the potential to further augment conformational entropy-structure discovery. The interface of experimental data, AlphaFold-type AI approaches, and physics-based molecular dynamics simulations will surely catalyze developments in quantifying conformational entropy . Machine learning is already realizing some of this immense potential for ensemble discovery and model generation across length scales in CryoEM . Understanding the relationship between latent spaces leveraged by these machine learning techniques and the underlying thermodynamics of the system may help guide the generative ensemble process to be even more predictive biologically. These methodological advancements are also necessitating better data structures than static PDB files to represent ensemble-level features . Advancing the modeling and encoding of data will facilitate breakthroughs in structural ensemble predictions, enabling more facile interpretation of the conformational ensemble and how it relates to conformational entropy.