HOMER (Helper Of My Eternal Retrievals) is a machine-learning-accelerated Bayesian inverse modeling code. Given some data and uncertainties, the code determines the posterior distribution of a model. HOMER uses MC3 (ascl:1610.013) for its MCMC; its forward model is a neural network surrogate model trained by MARGE (ascl:2003.010). The code produces plots of the 1D marginalized posteriors, 2D pairwise posteriors, and parameter history traces, and can also overplot the 1D and 2D posteriors for comparison with another posterior. HOMER computes the Bhattacharyya coefficient to compare the similarity of two 1D marginalized posteriors.