We train and evaluate a support vector machine (SVM) classifier for predicting the toxicity of pesticide molecules to bees. Enabling a machine learning approach, the BeeToxAI project compiled labeled data from bee toxicity experiments, composed of 382 (pesticide molecule, bee toxicity outcome) pairs. We compare two constructions of a molecular vector space for the SVM: (1) a random walk feature space, describing pesticides by the set of vertex-and edge-label sequences along length-L walks on their molecular graphs and (2) the MACCS fingerprint (FP) space, describing pesticides by the presence/absence of a list of pre-defined subgraph patterns in their molecular graphs. We explicitly construct the MACCS FPs but instead rely on the kernel trick and fixed-length-L random walk graph kernel (RWGK) for dot products in the random walk feature space. The L-RWGK-SVM achieves an F1 score (mean over 2000 runs) of 0.69 on the test data set, and L = 4 is the mode optimal walk length to describe the molecular graphs. The MACCS-FP-SVM performs on par/marginally better but exhibits more variance in its performance. Finally, we illuminate subgraphs in the pesticide molecules that tend to most strongly push molecules in the MACCS FP space towards the toxic/non-toxic side of the separating hyperplane of the MACCS-FP-SVM. The pesticide toxicity classification task. We wish to construct a classifier f : G → {-1, 1} that maps any molecular graph G (see Sec. 3.1) representing a pesticide molecule to a predicted binary label ŷ = f (G), where ŷ = 1 is toxic to honey bees (Apis mellifera) and ŷ = -1 is nontoxic. The classifier f is valuable as a cheap-to-evaluate "surrogate model" of an expensive bee toxicity experiment. See Fig. .