Environmental fingerprinting has been proposed as a key enabler to immersive, highly contextualized mobile computing applications, especially augmented reality. While fingerprints can be constructed in many domains (e.g., wireless RF, magnetic field, and motion patterns), visual fingerprinting is especially appealing due to the inherent heterogeneity in many indoor spaces. This visual diversity, however, is also its Achilles' heel - matching a unique visual signature against a database of millions requires either impractical computation for a mobile device, or to upload large quantities of visual data for cloud offload. Further, most visual "features" tend to be low entropy - e.g., homogeneous repetitions of floor and ceiling tiles. Our system VisualPrint, proposes a means to offload only the most distinctive visual data, that is, only those visual signatures which stand a good chance to yield a unique match. VisualPrint enables cloud-offloaded visual fingerprinting with efficacy comparable to using whole images, but with an order reduction in network transfer.