We propose a method that can reason and find hidden objects from a set of images. It discovers relationships between all objects detected by a backbone neural network. We focus on object searching and location determination in the real world. Given the name of an object, the system detects the object and outputs a bounding box containing it if it is in sight. Otherwise, the system outputs bounding boxes containing relevant objects around which the target object is most likely to appear. The primary process of the system includes training and reasoning. The former establishes system experience of object relations, and the latter implements object searching based on this experience. The system consists of a relational discovery module and a searching module. We have tested the proposed method on multiple datasets (COCO, PASCAL VOC, and ImageNet) and multiple backbone networks, and the results show that the proposed method has strong robustness and generalizability. The system can continuously highlight the most relevant objects in the line of sight, thus providing hints for approaching and locating the target objects until they are found, and the proposed method can meet the requirements of real time and accuracy if a suitable backbone network is selected.