Bayesian Neural Networks (BNNs) are a principled way to incorporate epistemic uncertainty into deep learning, and they play a significant role in out-of-distribution (OOD) detection, especially in settings where estimating predictive uncertainty is crucial. Empirical Bayesian methods, which initialize priors and surrogate posteriors from the weights of pretrained deterministic neural networks, can help in OOD detection by providing well-informed models, thereby bridging the gap between data-driven learning and principled uncertainty estimation—especially when true Bayesian inference is intractable. In this work, the empirical Bayes method MOdel Priors with Empirical Bayes using Deterministic neural networks (MOPED) is adapted to include a Gaussian mixture prior. Experiments on the medical datasets D7P and BreastMNIST, with OOD images containing artefacts such as rulers and annotations, demonstrate marked improvements in OOD detection from the proposed prior with predictive entropy as the score. The proposed empirical Bayes methods also performs on par with state-of-the art OOD measures.