In this paper, we discuss three wrapper multi-label feature selection methods based on the Random Forest paradigm. These variants differ in the way they consider label dependence within the feature selection process. To assess their performance, we conduct an extensive experimental comparison of these strategies against recently proposed approaches using seven benchmark multi-label data sets from different domains. Random Forest handles accurately the feature selection in the multi-label context. Surprisingly, taking into account the dependence between labels in the context of ensemble multi-label feature selection was not found very effective.