People combine their abstract knowledge about the world with data they have gathered in order to guide search and prediction in everyday life. We present a Bayesian model that formalizes knowledge transfer. Our model consists of two components: a hierarchical Bayesian model of learning and a Markov Decision Process modeling planning and search. An experiment tests qualitative predictions of the model, showing a strong ﬁt between human data and model predictions. We conclude by discussing relations to previous work and future directions.