Most existing robotic manipulation benchmarks focus on tabletop or household scenarios. While these setups have driven impressive progress, it remains unclear whether generalist VLAs that excel there can truly generalize to domains with different geometry, semantics, and workflows. We introduce RoboBenchMart, an open-source simulated benchmark targeting retail dark-store environments, where a mobile manipulator must perform complex manipulation tasks with diverse grocery items. This setting presents significant challenges, including dense object clutter and varied spatial configurations, with items positioned at different heights, depths, and in close proximity. By targeting on the retail domain, our benchmark addresses a setting with strong potential for near-term automation impact. Using generated trajectories, we model a standard, realistic fine-tuning setup for current generalist VLAs and evaluate several state-of-the-art models. We find that they still struggle even on common retail tasks, indicating that these models are not yet truly general across domains. To support further research, we release the RoboBenchMart suite, which includes a procedural store layout generator, a trajectory generation pipeline, evaluation tools, and fine-tuned baseline models.