Autonomous exploration and object search in unknown indoor environments remain challenging for multi-robot systems (MRS). Traditional approaches often rely on greedy frontier assignment strategies with limited inter-robot coordination. In this work, we present Coordinated Multi-Robot Exploration and Search using Vision Language Models (COMRES-VLM), a novel framework that leverages Vision Language Models (VLMs) for intelligent coordination of MRS tasked with efficient exploration and target object search. COMRES-VLM integrates real-time frontier cluster extraction and topological skeleton analysis with VLM reasoning over shared occupancy maps, robot states, and optional natural language priors, in order to generate globally consistent waypoint assignments. Extensive experiments in large-scale simulated indoor environments with up to six robots demonstrate that COMRES-VLM consistently outperforms state-of-the-art coordination methods, including Capacitated Vehicle Routing Problem (CVRP) and Voronoi-based planners, achieving 10.2\% faster exploration completion and 55.7\% higher object search efficiency. Notably, COMRES-VLM enables natural language-based object search capabilities, allowing human operators to provide high-level semantic guidance that traditional algorithms cannot interpret.