An SVM is a versatile supervised machine learning model that aims to find the maximum-margin separator (a hyperplane) between the positive and negative training examples in a mapped feature space. The mapped feature space does not need to be explicitly constructed. Instead, kernel functions can be used to [implicitly] perform the needed operation (dot product) in the mapped feature space. We compare two constructions of a molecular vector space by representing pesticide molecules with (1) the fixed-length random walk feature vector and (2) the MACCS fingerprint. The latter we explicitly construct, while for the former we rely on the fixed-length random walk graph kernel for the dot product.