State-of-the-art methods , which are based on deep learning, are able to integrate information from millions of reactions and building blocks, suggesting feasible synthetic routes for the majority of the benchmarked compounds in a matter of seconds to minutes . However, their outputs strongly depend on the employed databases and they tend to suggest multiple solutions which are difficult to rank and more importantly, even the fastest are computationally demanding and therefore ill-suited for high-throughput computational pipelines . Binary classifiers are machine learning algorithms trained to distinguish between compounds that are easy or difficult to make. Although the available approaches may differ in terms of learning algorithms (support vector machine, neural network, etc) and compound featurization (descriptors, fingerprints, etc.), it is the definition of the training set, consisting of compounds labelled as easy or difficult to make, that most impacts the behaviour of these methods. Some strategies for compiling training datasets include retrosynthesis , presence in commercial catalogues or virtually edited compounds . Although binary classifiers tend to be much faster than retrosynthesis-based methods, they are also less accurate and their performance is highly dependent on the training dataset . Binary classifiers also by definition cannot distinguish between different levels of difficulty . Complexity-based methods try to define an empirical metric under the assumption that complex molecules are more difficult to synthesize . Most methods define complexity as a function of the presence of features deemed to be complex or infrequent such as chiral centres, uncommon moieties, or unusual molecular fragments. One of the most popular measures of SA , SAscore 10 is a complexity-based method that uses the rarity of fragments found in PubChem and a set of predefined properties such as the ring complexity or the number of stereo centres to calculate its score. Another commonly used SA score, SCScore 11 employs an indirect estimation of complexity assuming that the complexity of the reactants is never larger than the complexity of the products. Due primarily to their simplicity and speed, SAscore and SCScore have been used extensively in drug development pipelines including for compound screening , dataset preparation and molecule generation/optimization . SAScore is one of the most popular metrics for biasing or discarding potentially infeasible compounds in methods for computational generation of de novo molecules 27,31-34 . However, as described above, SAscore and SCscore are simple approximations for SA and as such, present several limitations. For instance, it is well known that these scores tend to underestimate the SA of difficult compounds that can be synthesized from complex commercially available building blocks . It has also been shown that structurally similar compounds, which tend to have similar complexity-based scores, can require synthetic strategies of different difficulty levels , leading to incorrect SA estimations. Independent of their nature, the aim of all the methods described above is to computationally filter compounds, ruling out those difficult to make or purchase. This suggests the need of an alternative metric that directly estimates the actual metric of compound availability, namely its price. This is after all what influences many of the decisions in drug discovery, particularly in the early stages when the cost of the compounds to be experimentally tested is often of central importance. Current SA metrics exhibit poor correlation with prices, Fukunishi et al. found that the Pearson correlation coefficient (PCC) between their SA measurement and the logarithmic sales prices of the compound, in $/mmol, was ~0.3. Fernandez et al. observed only a weak correlation between prices and two complexity-based SA scores: Ring Complexity Index and SAscore. This is perhaps not surprising, since SA scores were never intended to capture price information. Nevertheless, most methods for automatic compound design try to optimize their molecules against a SA metric, which leads to the suggestion of many feasible yet prohibitively expensive compounds. For the hundreds of millions of compounds in the commercial catalogues, price estimation is merely a database search question that, in real situations can be severely delayed by the quotation process. However, the real challenge is estimating price for the rest of chemical space: the advent of machine learning-based molecular generation techniques and large virtual compound collections makes this problem increasingly acute. Compound cost prediction has previously been addressed using QSAR-like methods or retrosynthesisbased approaches . Fernandez et al. developed the QS$R approach, a classical machine learning method aimed to learn the relationship between the structure of the compounds and their prices for QSAR-like setups. As a proof-of-concept, the QS$R model was trained on ~4000 pairs of compound descriptors and prices, performing particularly well for compounds in the lower price range. Badowski et al. estimated the cost of a molecule as the cost of the cheapest retrosynthetic route considering the cost of the initial reactants, reactions yield, and fixed costs. While this formulation captures the different terms involved in the final price, it relies on the assumption that the cheapest retrosynthetic route is the one that determines the final cost, which does not necessary hold, and on estimations of reaction yields and fixed costs, information that is only available for a limited number of reactions and that, in many cases, is not in the public domain. With the aim of overcoming these problems, in this manuscript we present CoPriNet, a retrosynthesis-free method to obtain price predictions using only the compound itself. Our method is based on a graph neural network (GNN) trained on a dataset of >6M pairs of molecules and their prices collected from the Mcule 41 catalogue () and can be directly employed to assess novel compounds. Our approach follows that of SA binary classifiers trained on retrosynthesis predictions: given enough data, machine learning methods should identify patterns in the input molecules that are relevant for the synthetic planning (or the price) without the need to explicitly undergo retrosynthetic decomposition. Although retrosynthesisbased computations tend to be more accurate, our predictions exhibit a far stronger correlation with catalogue prices than any SA metric, with comparable running times. Consequently, our approach can be employed as a complementary metric to fast SA estimations for high throughput assays and more importantly, for de novo molecule generation, in which the large number of required assessments prevents retrosynthetic-based approaches from being used.