In this study, we suggest a method to adapt an image retrieval system into a configurable one. Basically, original feature space of a content-based retrieval system is nonlinearly transformed into a new space, where the distance between the feature vectors is adjusted by learning. The transformation is realized by Artificial Neural Network architecture. A cost function is defined for learning and optimized by simulated annealing method. Experiments are done on the texture image retrieval system, which use Gabor Filter features. The results indicate that configured image retrieval system is significantly better than the original system.