Matching two images with similar contents is one of the most fundamental tasks in image processing. Due to its importance, in recent years, many novel techniques have been proposed with great successes. Toward this effort, in this paper, we propose a radically different idea by bridging two seemingly unrelated fields - Image Processing and Biology - i.e., we propose to use the popular gene sequence alignment algorithm in Biology, BLAST, in determining the similarity between images. In this proposal, we map image features to a sequence of gene alphabets (e.g., A, C, G, and T in DNA, or 23 letters in protein) to utilize a wealth of advanced algorithms and tools in BLAST. Under the new idea, in particular, we study various image features and gene sequence generation methods that impact the accuracy and performance in matching similar images. Our proposal, termed as BLASTed Image Matching (BIM), is empirically validated using real data sets. Our work can be viewed as the "first" step toward bridging Image Processing and Biology fields in the application of the well-studied image matching problem.