We present a simple and effective method for similarity searching in virtual high-throughput screening, requiring only a string-based representation of the molecules (e.g., SMILES) and standard compression software, available on all modern desktop computers. This method utilizes the normalized compression distance, an approximation of the normalized information distance, based on the concept of Kolmogorov complexity. On representative data sets, we demonstrate that compression-based similarity searching can outperform standard similarity searching protocols, exemplified by the Tanimoto coefficient combined with a binary fingerprint representation and data fusion. Software to carry out compression-based similarity is available from our Web site at http://comp.chem.nottingham.ac.uk/download/zippity.