We develop a model of consumer search with spatial learning in which sampling the payoﬀ of one product causes consumers to update their beliefs about the payoﬀs of other products that are nearby in attribute space. Spatial learning gives rise to path dependence, as each new search decision depends on past experiences through the updating process. We present evidence of spatial learning in data that records online search for digital cameras. Consumers’ search paths tend to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. We estimate the structural parameters of the model and show that these patterns can be rationalized by our model, but not by a model without spatial learning. Eliminating spatial learning reduces consumer welfare by 12%: cross-product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for the power of search intermediaries. We use simulations to show that consumer-optimal product recommendations are that are most informative about other products.