Kotenseki is a collection of classical and ancient Japanese literature. It is comprised of image books that express Japanese stories by using comic drawings of different characters, such as humans, nature, and animals. To effectively store them for posterity, a search system is important. We propose an efficient CBIR system to assist the users in easily accessing the information and have an enjoyable experience browsing Kotenseki images. There are two main functions comprising keyword-based and image-based queries. We also provide automatic detection of objects within the original images to create a database of feature vectors. Our study utilizes the benefits of deep-feature and object-detection techniques. We also present our fine-tuned model, which adds L2 normalization, dropout, and data augmentation to obtain better accuracy, which can reach 84% of mAP in our Kotenseki dataset.