BayesBag has been established as a useful tool for robust Bayesian model selection. However, computing BayesBag can be prohibitively expensive for large datasets. Here, we propose a fast approximation of BayesBag model selection. This approximation---based on Taylor approximations of the log marginal likelihood---can achieve results comparable to BayesBag in a fraction of the time.