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We propose a new synthesis algorithm that canefficientlysearch programs withlocalvariables (e.g., those introduced by lambdas). Prior bottom-up synthesis algorithms are not able to evaluate programs withfree local variables, and therefore cannot effectively reduce the search space of such programs (e.g., using standard observational equivalence reduction techniques), making synthesis slow. Our algorithm can reduce the space of programs with local variables. The key idea, dubbedlifted interpretation, is to lift up the program interpretation process, from evaluating one program at a time to simultaneously evaluating all programs from a grammar. Lifted interpretation provides a mechanism to systematically enumerate all binding contexts for local variables, thereby enabling us to evaluate and reduce the space of programs with local variables. Our ideas are instantiated in the domain of web automation. The resulting tool,Arborist, can automate a significantly broader range of challenging tasks more efficiently than state-of-the-art techniques includingWebRobotand Helena.