This paper introduces a scoring metric and establishes an evaluation criterion for chemical synthesis. We present a scoring network for multi-step synthesis route design and selection that is trained to predict the estimated distance or similarity to the experimentally demonstrated routes. The scoring model is able to take advantage of different factors of the synthesis route evaluation including the complexity of intermediates or cost to perform the reaction, as well as the reaction molecular representation and thus implicitly learned reaction feasibility. We also performed validation experiments on the model using different methods of embedding to assess its prediction power. The top-1 ranking accuracy reaches 60.2% with a prediction error of 2.72 (RMSE) for SDF embedding.