Drug design routinely involves modification of chemical structure to improve molecular properties. Over decades, large structure/property datasets have been generated at great expense, but translating such data into solutions for drug design problems is a challenge. To translate large volumes of raw structure/property data into human-readable insights, computational platforms for matched molecular pair analysis have been developed. Matched molecular pair (MMP) is defined as two structures related by a defined transform in structure, such as ethane (CH 3 CH 3 ) and ethanol (CH 3 CH 2 OH) being related by transform of H to OH. MMPs and their transforms can be found automatically via unsupervised MMP identification algorithms, which can identify useful transforms that a human might not have thought of, but which will also find many irrelevant transforms to a given problem. Therefore, search algorithms are required for mining the results of unsupervised MMP identification on large datasets.