embedding,
                    boolean_filter,
                    k=k,
                    vector_field=vector_field,
                    subquery_clause=subquery_clause,
                )
            elif efficient_filter != {}:
                search_query = _approximate_search_query_with_efficient_filter(
                    embedding, efficient_filter, k=k, vector_field=vector_field
                )
            elif lucene_filter != {}:
                warnings.warn(
                    "`lucene_filter` is deprecated. Please use the keyword argument"
                    " `efficient_filter`"
                )
                search_query = _approximate_search_query_with_efficient_filter(
                    embedding, lucene_filter, k=k, vector_field=vector_field
                )
            else:
                search_query = _default_approximate_search_query(
                    embedding, k=k, vector_field=vector_field
                )
        elif search_type == SCRIPT_SCORING_SEARCH:
            space_type = _get_kwargs_value(kwargs, "space_type", "l2")
            pre_filter = _get_kwargs_value(kwargs, "pre_filter", MATCH_ALL_QUERY)
            search_query = _default_script_query(
                embedding, k, space_type, pre_filter, vector_field
            )
        elif search_type == PAINLESS_SCRIPTING_SEARCH:
            space_type = _get_kwargs_value(kwargs, "space_type", "l2Squared")
            pre_filter = _get_kwargs_value(kwargs, "pre_filter", MATCH_ALL_QUERY)
            search_query = _default_painless_scripting_query(
                embedding, k, space_type, pre_filter, vector_field
            )
        else:
            raise ValueError("Invalid `search_type` provided as an argument")
        response = self.client.search(index=self.index_name, body=search_query)
        return [hit for hit in response["hits"]["hits"]]