We trained and evaluated a support vector machine classifier that predicts the toxicity of pesticides to honey bees. We compared two molecular vector representations: (1) MACCS fingerprints listing the presence/absence of a set of pre-defined subgraph patterns in the molecular graph and (2) a random walk feature vector listing counts of label sequences along all fixed-length walks on the molecular graph. While we explicitly construct the fingerprints, we relied on the fixed-length random walk graph kernel for dot products in the random walk vector space. The classifier using the MACCS fingerprints (a) gave a slightly higher mean F1 score (0.70 vs. 0.69) than the classifier using the random walk feature, (b) grants a degree of interpretability, but (c) exhibits a higher variance in performance.