Bayesian networks learning from data has attracted a great deal of research over the last decades. The usual approaches to accomplishing this task combine two elements. The first one is a heuristic search procedure to generate candidate solutions and the other element is a scoring metric to evaluate each obtained solution based on the likelihood of the network, that can be interpreted as a probability of observing the data set under a given network model. In this paper, we propose the use of an Artificial Immune System as the search procedure for obtaining high-quality Bayesian networks, motivated by the multimodal search capability of these algorithms combined with the dynamical control of the population size and diversity along the search. We demonstrate the applicability of the proposal on two benchmarks and promising results were obtained.