In the chemoinformatics context, graphs have become very popular for the representation of molecules. However, a lot of algorithms handling graphs are computationally very expensive. In this paper we focus on outerplanar graphs, a class of graphs that is able to represent the majority of molecules. We define a metric on outerplanar graphs that is based on finding a maximum common subgraph and we present an algorithm that runs in polynomial time. Having an efficiently computable metric on molecules can improve the virtual screening of molecular databases significantly.