It has been shown in prior literature that BERT-based models pre-trained on general domain text may not perform well when used downstream with highly domain-specific data sets. Considerably better in-domain end-task performance can be obtained when the model is pre-trained "from scratch" on the domain-specific corpora, or a continual pre-training is applied, where the model is first pre-trained on general text and then the pre-training continues with the unlabelled domain-oriented corpora. Another significant consideration for pre-training, especially in the context of the predictive synthesis, is the availability of high quality data. Currently, both the publicly and commercially available reaction data sets are limited in size and typically require extensive filtration and clean-up in order to afford a collection of chemically valid and complete reaction schemes. The use of commercial data sets can also incur a considerable financial cost. We thus envisaged the creation of completely free, artificial and domain-specific reaction data sets of potentially unlimited sizes, which could be used for indomain model pre-training, specifically tailored to the desired end-task at hand. To this end, compounds sampled from the publicly available ZINC database, were filtered and used as target products of the artificial Buchwald-Hartwig or Suzuki-Miyaura reactions. ZINC compounds with unprotected functionalities were duplicated and the copies augmented with different protective groups. The selected compounds were then disconnected using SMARTS-based reaction rules for the corresponding coupling reactions, generating simple reaction schemes with different possible combinations of reactants for each product. Transformation-specific catalysts, ligands, bases and solvents were sampled from the public HTE data sets, and added to the artificially generated schemes. Such created reactions were then standardised, resulting in two domain-specific data sets appropriate for the downstream prediction tasks on the Buchwald-Hartwig and Suzuki HTE data. The in-domain pre-training with the completely artificial 1.68M reactions afforded generally good end-task performance, statistically better than models with no pre-training for the majority of tested splits (Figures 7, 8 and S34-36, Tables ). However, the artificially pre-trained models typically underperformed in comparison to the models pre-trained on the real Pistachio data. The most significant difference between these approaches, in favour of the standard pre-training, was observed for the split number five of the Buchwald-Hartwig set from Ahneman et al. We believe that the small sacrifice in performance is completely justified by no financial costs associated with the data sourcing and markedly faster preparation of the model-ready data than with the currently available experimental reaction sets. Depending on the practitioner's expectations, the pre-training relying solely on the self-generated examples could be fully sufficient to prepare the model for the desired end-task prediction. The observed small drop in performance in comparison to the laboratory data could have at least two plausible explanations. As discussed above, the literature and patent data are highly skewed towards successful combinations of reactants and reagents, whereas, the artificial sets are expected to contain substantially more negative examples, if these reactions were carried out in a laboratory. Thus, the artificially generated sets lose all this statistical information contained in the real data sets. Additionally, the ZINC data base contains screening compounds which are on the larger and more complex end of the synthetic range encountered in a medicinal chemistry lab. The median molecular weight for the products in the Pistachio set was 360 Da, but it was 383 and 385 Da in the artificial Buchwald and Suzuki sets, respectively. Molecular complexity of the products was determined using size-normalised Spacial Score (nSPS), which considers different topological factors of the analysed structures. The median nSPS for Pistachio was 14.3 units, but it rose considerably to 16.8 and 15.5 for the artificial sets, indicating higher structural complexity in these collections (Figure ). Considering the data sources of the NextMove's Pistachio set, it contains compounds with presumably more synthetically representative characteristics than molecules in reactions generated based on the ZINC structures. To account for these potential shortcomings, we employed a continual pre-training approach, first, pre-training models with the full Pistachio set for 30 epochs and then training the resulting models on the domain-specific artificial data for a limited number of MLM steps. The generated models gave no improvement over the baseline (Figures S34-36, Tables ). We then decided to combine the data from Pistachio and the domain-specific artificial set in equal proportions, to create a hybrid collection of 1.68M reactions. The models pre-trained with the augmented data achieved at least as good performance as the models-pretrained with the real data or better (Figures and S34-36, Tables ). The models were generally comparable for the regression task with the Buchwald-Hartwig set (mean R 2 of 0.701 for 1.68M Pistachio set vs. 0.715 for the augmented set for all splits), but the pre-training with the augmented data resulted on the whole in superior performance when evaluated on the remaining tasks. The classification with the Buchwald-Hartwig reactions gave average Cohen's Kappa across splits of 0.713 and 0.755 for the full Pistachio and the augmented sets, respectively. Evaluation of regression with the Suzuki set gave mean R 2 of 0.22 for the full Pistachio set and 0.406 for the augmented set. Reaction success classification afforded Cohen's Kappa of 0.277 for Pistachio and 0.334 for the hybrid set. Thus, this approach to the data augmentation seems as a simple, quick and cost-effective way to improve the overall performance of the models.