Imitation learning (IL) algorithms typically distill demonstrations into parametric policies to mimic expert behavior. However, with limited data and partial observability, such as in egocentric mobile manipulation, existing methods often struggle to generate accurate actions. To address these challenges, we propose ReMoBot, a few-shot, trajectory-conditioned imitation learning framework that directly Retrieves information from demonstrations to solve Mobile manipulation tasks with ego-centric visual observations. Leveraging vision foundation models, ReMoBot identifies relevant expert demonstrations by combining state-level similarity, history-aware trajectory alignment, and action-sequence consistency to disambiguate perceptually similar observations. The agent then selects appropriate control commands based on these retrieved demonstrations in a fully training-free manner. We evaluate ReMoBot on three mobile manipulation tasks using a Boston Dynamics Spot robot in both simulation and real-world settings. After benchmarking five approaches in simulation, we compare our method with two baselines trained directly on real-world data without sim-to-real transfer. With only 20 demonstrations per task, ReMoBot outperforms the baselines, achieving high success rates in Table Uncover (70%) and Gap Cover (80%), while also showing promising performance on the more challenging Curtain Open task in the real-world setting. Furthermore, ReMoBot generalizes across varying robot positions, object sizes, and material properties, highlighting its robustness in real-world deformable mobile manipulation. Additional details are available at: https://sites.google.com/view/remobot/home