Suggesting novel compounds to be made on the basis of similarity to a previously seen structure-activity relationship (SAR) requires a measure for SAR similarity. While SAR similarity has intuitively been used by medicinal chemists for decades, no systematic comparison of candidate similarity metrics has been published to date. With this publication, we attempt to close that gap by providing a statistical framework that allows comparison of SAR similarity metrics by their ability to rank series that provide the best activity prediction of novel substituents. This prediction is a result of a two-step process that involves (a) judging the similarity between series and (b) transferring the SAR from one series to the other. We tested several SAR similarity metrics and found that a centered RMSD (cRMSD) in combination with a lineaar-regression-based prediction interpolation ranks SAR profiles best. Based on that ranking we can, with a given confidence, suggest novel substituents to be tested. The superiority of the cRMSD can be explained on the basis of experimental uncertainty of affinity data and measured affinity differences. The ability to measure SAR similarity is central to applications like matched molecular series (MMS) analysis, whose applicability depends on whether there is a potential for SAR transferability between series. With the new SAR similarity metric introduced here, we show how MMS can be used in a medicinal chemistry setting as an idea generator and a semiquantitative prediction tool.