Price estimation from retrosynthesis predictions was performed using a simple heuristic as a replacement for the non-publicly available method proposed by Badowski et all . This heuristic only considers the cost of the building blocks neglecting any additional cost. Thus, taking into account that the retrosynthesis results obtained for each compound tend to include several pathways, potentially involving multiple building blocks, we employed two simple strategies. The first one assumes ideal route ranking, thus overestimates the performance (ignoring non-reactant costs) by selecting the route that best matched our price records. The second strategy just reports the minimum price route and should provide an underestimation of the performance. The QS$R multilayer perceptron model was reimplemented as indicated in Fernandez et al. with the exception of descriptors calculation, that were computed The node and edge vectors are projected into higher dimensionality embeddings (coloured rectangles within GNN box) using independent learnable weights for the nodes (Linear-nodes) and for the edges (Linear-edges). After that, the node and edge embeddings are processed by ten blocks of PNA layer, batch normalization and ReLU activation, updating the state of the node embeddings after each block. Then, the processed embeddings of all the nodes are combined into one single graph embedding using a Set2Set layer . Finally, the graph embedding is processed by two blocks of linear layer, batch normalization and ReLU activation from which the price prediction is obtained using a linear layer with one single unit.