The developed framework provides materials scientists with a quantitative tool for assessing candidate materials against existing knowledge, enabling more informed selection of synthesis targets. This approach has potential applications in active learning strategies, where novelty metrics could guide exploration of undersampled regions of the materials space. Such application could enhance the diversity of training data and improve model robustness in previously unexplored domains. Future work might explore the integration of this novelty estimation approach with performance prediction models to optimize the balance between novelty and practical utility in materials discovery campaigns.