The proposed European Union regulations for Artificial Intelligence (AI) has highlighted the necessity and importance of explainable AI as a way for understanding the output and working basis of current AI systems. In the context of recommender systems as popular AI-based tools focused on providing users with the items that best fit their preferences and needs in a search space overloaded with possible choices, the development of explainable recommendation approaches currently has an increasing interest nowadays. However, most of the research efforts are focused on explaining individual recommendations. In contrast, the current contribution is centered on developing a novel approach for generating post-hoc explanations over the output of group recommender systems. To accomplish this goal, it is used the local rule-based explanation approach (LORE), popular in machine learning-based settings. The development of an experimental protocol based on item tags for evaluating the proposed approach in terms of model fidelity and feature coverage rate, points out that it is able to achieve appropriate fidelity values, while covers most of the features used for characterizing items.