Our focus here has been on efficient graph planning given an input set of similarity scores, but this means that similarity scoring remains for further consideration and is an active area of interest in the field. For example, recent work has made progress in predicting perturbation reliability in RBFE calculations using machine learning . These results could be used to inform similarity scores in HiMap. In addition, with large-scale computing resources available, the curation of data sets to further investigate similarity metrics is becoming tractable and hopefully will be an area of future work.