Search has become an integral part of Adobe products and users rely on it to learn about tool usage, shortcuts, quick links, and ways to add creative effects and to find assets such as backgrounds, templates, and fonts. Within applications such as Photoshop and Illustrator, users express domain-specific search intents via short text queries. In this work, we leverage sentence-BERT models fine-tuned on Adobe's HelpX data to perform multi-lingual semantic search on help and tutorial documents. We used behavioral data (queries, clicks, and impressions) and additional annotated data to train several BERT-based models for scoring query-document pairs for semantic similarity. We benchmarked the keyword-based production system against semantic search. Subsequent AB tests demonstrate that this approach improves engagement for longer queries while reducing null results significantly.