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Instance retrieval systems are widely used in applications such as robot navigation, medical diagnosis, and augmented reality. Blippar is a company that creates compelling augmented reality experiences or provides you with the tools to build your own. In this paper we focus on one of the company's augmented-reality applications, with which users are able to point their phone cameras at different objects in order to receive information about the objects in real time. In this paper, we provide what we believe to be the first study of forward index compression techniques for such instance retrieval systems. First, we perform an analysis of real-world data from a large-scale commercial instance retrieval system, run by Blippar focusing on augmented reality. Then we propose an entropy-based lossless compression strategy. Experiments show that our proposed Huffman-based approach outperforms a variety of other compression techniques, while also increasing overall system efficiency slightly.