Yield prediction for synthetic reactions is one of the holy grails of computational chemistry. Accurate prediction of the yield remains a truly challenging and largely unsolved task, due to the incredibly vast chemical space available for each of the reaction components, sensitivity of the reaction outcome to the reaction conditions, limited access to high-quality data for model building, significant dependence of the reactions on operational conditions and human operators causing high variance in the results. Reactivity cliffs further increase the difficulty for building reliable yield-prediction models, where structurally similar compounds can be characterised by starkly different reaction success outcomes. Despite the plethora of challenges associated with the yield prediction using the currently available reaction data sets, a number of research groups have attempted this feat, basing their approaches on various machine learning (ML) techniques and architectures, including decision trees and random forests, feed-forward neural networks (NNs), Graph NNs (GNNs), Transformers, and Transformer-based Encoders. The Encoder models typically either outperform other solutions or have comparable performance to the alternatives, and Bidirectional Encoder Representations from Transformers (BERT) is the main Encoder architecture used for the reaction success prediction. BERT is designed to be pre-trained through a self-supervised training approach using unlabelled data, and then it is fine-tuned with labelled examples on a downstream, task-specific problem. As the pretraining does not require labels, the network can be pre-trained using only the tokenized text-based reaction schemes extracted from literature and patent data, removing the reliance of the model on the noisy and often poorly reproducible yield labels. Mask language modelling (MLM) is normally used by BERT as the self-supervised task, where some of the input tokens are masked and some are replaced by random tokens, and the network learns to predict the original identity of the masked input components. Subsequently, fine-tuning can be performed with high-quality focused reaction data sets, using a standard classification or regression approach. The first application of BERT to reaction yield prediction was presented by Schwaller and colleagues, who used the basic BERT architecture pretrained on the United States Patent and Trademark Office (USPTO) reaction Simplified Molecular-Input Line-Entry System (SMILES) data and fine-tuned on either patent data or High-Throughput Experimentation (HTE) results. The resulting model was dubbed as YieldBERT. YieldBERT-DA is a modification of the model, training with augmented data, where the SMILES representations of the molecules are randomised and different permutations of components in the reaction schemes are used. Neves et al., 2023, improved the YieldBERT model by extending the embedding layer of BERT, allowing to input encoded information about the reaction component role and identity or reaction conditions to the model. The resulting BERT-Enriched-Embedding (BEE) model was pre-trained on millions of patent and literature data points, fine-tuned on 750K unique reactions from the proprietary Janssen electronic lab notebooks (ELNs) and used as a classifier of the reaction success. As far as we are aware, BEE is the first global model, not specific to a single chemical transformation, with good enough performance to be fit for practical applications in organic chemistry labs and, as estimated by the BEE authors, potentially reducing Janssen's rate of the negative reactions by over 30%.