Efficient navigation and search in unknown environments for multiple objects is a fundamental challenge in robotics, particularly in applications such as warehouse management, domestic assistance, and search-and-rescue. The Multi-Object Search (MOS) problem involves navigating to a sequence of locations to maximize the likelihood of finding target objects while minimizing travel costs. In this paper, we introduce a novel approach to the MOS problem, called Finder, which leverages vision language models (VLMs) to locate multiple objects across diverse environments. Specifically, our approach introduces multi-channel score maps to track and reason multiple objects simultaneously during navigation, along with a score map technique that combines scene-level and object-level semantic correlations. We validate our approach through extensive experiments in both simulated and real-world environments. The results demonstrate that Finder outperforms existing multi-object search methods using deep reinforcement learning and VLM Additional ablation and scalability studies highlight the importance of our design choices and show the system’s robustness with increasing number of target objects. Website: https://find-all-my-things.github.io/