With the advent of deep networks, most computer vision tasks have been revolutionized. Image retrieval is no exception from this migration. The use of a stack of linear convolutional operations makes a convolutional neural network (CNN) a powerful tool in computer vision. Morphological operations are powerful non-linear topological operators able to include morphological features and, therefore, capable of enabling a deep convolutional network to capture more informative features. Motivated by these advantages, this paper proposes a deep image retrieval technique based on the residual block using morphological operations. The proposed residual network is tested on various benchmark databases to validate the performance of the proposed model for image retrieval. The results show that our method has superior performance compared to other baseline approaches.