The diversity of the compound libraries was computed with the extended similarity indices as recently reported. Briefly, instead of only measuring the similarity between pairs of molecules, the extended (e.g., n-ary) indices allow us to calculate the similarity of any number of molecules simultaneously. This has two key advantages when it comes to the analysis of molecular libraries. First, the n-ary indices provide a truly global description of the correlation between the molecules in the set (as exemplified by their superior performance in estimating the compactness of a set. Second, the extended indices are dramatically more efficient, requiring only O(N) operations to calculate the similarity of N molecules (opposed to the quadratic scaling of the standard binary similarity indices). Extended similarity has been successfully used in numerous applications, including compound diversity analysis, comparison of nucleotide and protein sequences, and, more recently, analysis of molecular dynamics simulations. Herein, we used this approach as a novel and efficient manner to quantify and compare the fingerprintbased diversity of the 11 compound libraries. The extended similarity indices were computed with a fractional weight function, with various coincidence thresholds. The Python code to conduct the extended similarity indices calculations is freely available at .