The Ant Colony Optimization (ACO) meta-heuristic is a bio-inspired approach where a colony of artificial ants cooperates via pheromone trails in order to solve a combinatorial optimization problem. Intuitively, a key point when developing any ACO algorithm is to define the pheromonal strategy, i.e., to decide where pheromone trails should be laid and how they should be exploited and updated during the solution process. In this paper, we study the influence of considering different pheromonal strategies on the ant search capabilities when solving multivalent graph matching problems. Two different ACO algorithms, with respect to two different pheromonal strategies, are proposed and experimentally compared.