Temporal information is increasingly available with network data sets. This information can expose underlying processes in the data via sequences of link activations. Examples range from the propagation of ideas through a scientific collaboration network, to the spread of disease via contacts between infected and susceptible individuals. We focus on the flow of funds through an online financial transaction network, in which given patterns might signify suspicious behaviour. The search for these patterns may be formulated as a temporally constrained subgraph isomorphism problem. We compare two algorithms which use temporal data at different stages during the search, and empirically demonstrate one to be significantly more efficient.