Despite the existence of many approaches to reference-condition modeling, Bayesian statistical methods have not been used. We assessed whether a hybrid approach that combined features of existing reference-condition approaches with Bayesian model fitting and assessment of test sites could provide superior results to existing established methods. We used 4 Bayesian models of increasing complexity to develop and test reference-condition models for 5 biotic endpoints across 3 data sets. Our best models were comparable or superior to standard approaches (Benthic Assessment of Sediment, Australian River Assessment System) using the same data. Those of our models with the simplest endpoint (species richness) performed best. On average, those models with the simplest model structures also performed best, but differences in performance among models of different complexity were small. All models performed poorly at detecting the lower levels of simulated impact in the test data. However, these impacts were small relative to the variation among validation sites and consequent predictive uncertainty of the models. The Bayesian approach to reference-condition modeling shows promise as an alternative to existing methods. It also has advantages in terms of the ease of interpretation of model outputs. However, for the approach to be relevant, further development work should be driven by a perceived need to revise standard methods used by management agencies.