Group recommenders must balance diverse and sometimes conflicting preferences. This work uses Monte Carlo Tree Search to explore candidate recommendation sequences and optimize collective satisfaction, including beyond-accuracy properties such as diversity, novelty, and fairness. It also studies the integration of large language models into the search process. Preliminary experiments indicate improvements over traditional aggregation techniques, with LLM integration producing positive results for recommendation relevance.