Similarity metrics are at the heart of spectral library search procedures that are used to identify unknown substances. The problem is relatively easy when the query spectrum (that is, a spectrum of the substance to be identified) is directly represented in the library, but in general this is not the case, and the query spectrum may come from a mixture of substances that are either individually represented in the library or as a mixture. In such cases, employing standard search metrics may not yield good results. A well-known general strategy to improve search is to design domain-specific metrics that capture its intrinsic properties. In this paper, we present a new Raman spectroscopy specific spectral similarity metric, Spectral Linear Kernel, which captures the domain subtleties while performing spectral search and performs better in comparison to standard spectral search methods. We also present a new modified Euclidean measure which not only performs better than the standard Euclidean method but other standard methods. We evaluate our results on Raman spectroscopy data for chlorinated solvents. (C) 2012 Elsevier B.V. All rights reserved.