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Logo search is widely required in many real-world applications. As a special case of near-duplicate images, logo pictures have some particular properties, for instance, suffering from flipping operations, e.g., geometry-inverted and brightness-inverted operations. Such operations completely change the spatial structure of local descriptors, such as SIFT, so that image search algorithms based on Bag-of-Visual-Words (BoVW) often fail to retrieve the flipped logos. We propose a novel descriptor named Max-SIFT, which finds the maximal SIFT value sequence for detecting flipping operations. Compared with previous algorithms, our algorithm is extremely easy to implement yet very efficient to carry out. We evaluate the improved descriptor on a large-scale Web logo search dataset, and demonstrate that our method enjoys good performance and low computational costs.