Molecular machines visit a continuum of conformational states as they go through work cycles required for their metabolic functions. Single-molecule cryo-EM of suitable in vitro systems affords the ability to collect a large ensemble of projections depicting the continuum of structures and assign occupancies, or free energies, to the observed states.Through the use of machine learning and dimension reduction algorithms it is possible to determine a low-dimensional free energy landscape from such data, allowing the basis for molecular function to be elucidated. In the absence of ground truth data, testing and validation of such methods is quite difficult, however. In this work, we propose a workflow for generating simulated cryo-EM data from an atomic model subjected to conformational changes. As an example, an ensemble of structures and their multiple projections was created from heat shock protein Hsp90 with two defined conformational degrees of freedom. All scripts for reproducing this workflow are available online. 1