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In this paper, we present an extremely fast method for online image retrieval of JPEG compressed images. We exploit minimal perceptual error image compression which optimises JPEG quantisation tables to improve the resulting image quality. In particular, we demonstrate that thus tuned quantisation tables can be used as image descriptors for performing content-based image retrieval. Image similarity is expressed as similarity between the respective quantisation tables and feature extraction and comparison be performed in an extremely fast fashion as it is based on information only from the JPEG headers. We show that our method takes only about 2-2.5% of the time of standard compressed domain algorithms, yet achieves retrieval accuracy within 3.5% of these techniques on a large dataset.