In this work, we present a method of producing image descriptors that is based on max-pooling of sparse codes. We use this method on images from the Solar Dynamics Observatory (SDO). The SDO produces over 70,000 images of the Sun each day, and with so many images being archived, an efficient method for finding similar images in this ever growing dataset is critical. Our method for producing descriptors is advantageous because the results are of a reasonable size for indexing, and are more selective than other methods used in the past. We use sparse coding on learned dictionaries to produce linear decompositions of the input signals. These decompositions are unlike decompositions based on principal component analysis, as we do not impose the constraint that basis vectors be orthogonal. By removing the orthogonal constraint, we are able to more easily adapt the representation to the data, and we show that our initial retrieval results alleviate a problem found to be an issue for this dataset. Specifically, the problem of the immediate temporal neighbor being the most similar in virtually every case.