Recommender systems are prone to various biases. Hence, bias mitigation approaches are needed to counteract those. In the music sector, gender imbalance is a particular topical subject. Earlier work has shown that the gender imbalance in the sector translates to the output of music recommender systems. Several works emphasize that items representing women should be given more exposure in music recommendations. In this work, we present an exploratory analysis of several bias mitigation strategies. Using a simulation approach, we explore the effects of different pre- and post-processing strategies for bias mitigation. We provide an in-depth analysis using state-of-the-art performance measures and metrics concerning gender fairness. The results indicate that the different strategies can help to mitigate gender bias in the long term in particular ways: Some strategies’ render improvement in exposure of women in the top ranks; other approaches help recommending more variety of items representing women.