We are currently witnessing growing concerns regarding fairness in online marketplace environments, particularly in situations involving platform-owned first-party selling, biased recommendation algorithms and trust formation. Regulatory measures, such as those imposed by the European Commission and the US Federal Trade Commission, underscore the importance of fair practices on these platforms. While machine learning ranking models are widely used in ranking multiple offers from various sellers, they can inadvertently introduce biases, raising regulators’ concerns. Despite recent advances in fair ranking algorithms, the long-term impact of fairness in ranking remains understudied. The contributions of this research are threefold: 1) it establishes a simulated online marketplace environment to complement traditional static experiments; 2) it assesses the long-term impact of fairness in utility of ranking algorithms; and 3) it evaluates state-of-the-art fairness techniques in dynamic environments. Findings reveal how utility in ranking fairness algorithms can be affected by the application of fairness techniques and how data drift impacts regular and fair ranking algorithms in a long-term scenario.