EMMIVox is a Bayesian inference approach designed to generate high-quality structural models from cryo-electron microscopy (cryo-EM) data. Unlike existing methods that often prioritize data fit over structural quality, EMMIVox balances experimental information with accurate physico-chemical models, including waters, lipids, and ions. By explicitly treating data correlation and noise, and inferring accurate B-factors, EMMIVox produces models with excellent data fit and high stereochemical quality. The method is flexible and can be used for both single-structure and ensemble refinement, outperforming state-of-the-art techniques. EMMIVox is implemented in the PLUMED library and is available on GitHub. The authors benchmarked EMMIVox on nine complex biological systems, demonstrating its superior performance in terms of various quality metrics. EMMIVox also effectively handles medium-low resolution cryo-EM data, refining coarse-grained models of large protein complexes and determining conformational ensembles that capture structural heterogeneity. The method is particularly useful for extracting dynamic properties of proteins, lipids, ligands, waters, and ions from cryo-EM density maps, advancing our understanding of molecular mechanisms and supporting applications such as structure-based drug design and machine learning.EMMIVox is a Bayesian inference approach designed to generate high-quality structural models from cryo-electron microscopy (cryo-EM) data. Unlike existing methods that often prioritize data fit over structural quality, EMMIVox balances experimental information with accurate physico-chemical models, including waters, lipids, and ions. By explicitly treating data correlation and noise, and inferring accurate B-factors, EMMIVox produces models with excellent data fit and high stereochemical quality. The method is flexible and can be used for both single-structure and ensemble refinement, outperforming state-of-the-art techniques. EMMIVox is implemented in the PLUMED library and is available on GitHub. The authors benchmarked EMMIVox on nine complex biological systems, demonstrating its superior performance in terms of various quality metrics. EMMIVox also effectively handles medium-low resolution cryo-EM data, refining coarse-grained models of large protein complexes and determining conformational ensembles that capture structural heterogeneity. The method is particularly useful for extracting dynamic properties of proteins, lipids, ligands, waters, and ions from cryo-EM density maps, advancing our understanding of molecular mechanisms and supporting applications such as structure-based drug design and machine learning.