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In the medical domain, experts usually look at specific anatomical structures to identify the cause of a pathology, and therefore they can largely benefit from automated tools that retrieve relevant slice(s) from a patient's image volume in diagnosis. Accordingly, this paper introduces a novel search and retrieval work for finding relevant slices in brain MR (magnetic resonance) volumes. As intensity is non-standard in MR we explore performance of two complementary intensity invariant features, local binary patterns and Kanade-Lucas-Tomasi feature points, their extended versions with spatial context, and a simple edge descriptor with spatial context. Experiments on real and simulated data showed that the local binary patterns with spatial context is fast, highly accurate, and robust to geometric deformations and intensity variations.