In this paper, we study the ability of content-based image retrieval by extracting descriptors from a deep neural network (DNN) trained for classification purposes. We fine-tuned the VGG model for the weeds classification task. Then, the feature vector, also a descriptor of the image, is obtained from a global average pooling (GAP) and two fully connected (FC) layers of the VGG model. We apply the principal component analysis (PCA) and develop an autoencoder network to reduce the dimension of descriptors to 32, 64, 128, and 256 dimensions. We experiment weeds species retrieval problem on the Chonnam National University (CNU) weeds dataset. The experiment shows that collecting features from DNN trained for weeds classification task can perform well on image retrieval. Without applying dimensionality reduction techniques, we get 0.97693 on the mean average precision (mAP) value. Using autoencoder to reduced dimensional descriptors, we achieve 0.97719 mAP with the descriptor dimension is 256.